------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\Strongman-Rep
> ression.log
  log type:  text
 opened on:  27 Nov 2018, 12:59:57

. 
.                 set more off 

.                 set matsize 1000

.                 global seed ="984353"

. 
.                 
. ***********************************
. **** Clean and merge data sets ****
. ***********************************
. 
.                 * Clean covariate data sets *
.                         qui do clean-prio

.                         qui do clean-nmc

.                         qui do clean-ji

.                         qui do clean-nelda  /* downloaded 3.8.2018 */

.                         qui do clean-urdal /* downloaded from https://www.prio.org/Publication
> s/Publication/?x=357 on 3.16.2018 */

. 
.                 * Coup data *
.                         set more off

.                         insheet using http://www.uky.edu/~clthyn2/coup_data/powell_thyne_coups
> _final.txt, clear  /* downloaded 3.8.2018 */
(7 vars, 474 obs)

.                         rename ccode cowcode

.                         rename country pt_country

.                         * 1st coup in Yemen (ccode=680) 1968 is South Yemen but the second cou
> p in August is actually in N/All-Yemen
.                         recode cow (679=678)
(cowcode: 0 changes made)

.                         recode cow (680=678) if year==1968 & month==8
(cowcode: 1 changes made)

.                         gen  p = "/"

.                         egen d = concat(day p month p year)

.                         gen date  = date(d, "DMY")

.                         gen coupA = coup==1

.                         gen coupS = coup==2

.                         drop p d

.                         local var = "coup coupA coupS"

.                         foreach v of local var {
  2.                                 bysort cow year: egen max`v'=max(`v')
  3.                                 replace `v' =max`v'
  4.                                 drop max`v'
  5.                         }
(27 real changes made)
(26 real changes made)
(27 real changes made)

.                         egen tag= tag(cow year)

.                         keep if tag==1
(64 observations deleted)

.                         drop tag

.                         tsset cow year
       panel variable:  cowcode (unbalanced)
        time variable:  year, 1950 to 2017, but with gaps
                delta:  1 unit

.                         sort cow year

.                         saveold coups-merge,replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in older Stata formats)
file coups-merge.dta saved

.                         
.                 * EPR data *
.                         use epr-original, clear
(epr v3.01 country level data (31 Dec 2014))

.                         recode cow (260=255) (679=678) /* Yemen and West Germany are continuou
> s regimes across unification */
(cowcode: 56 changes made)

.                         tsset cow year
       panel variable:  cowcode (unbalanced)
        time variable:  year, 1946 to 2010
                delta:  1 year

.                         gen e = l.ethfrac
(155 missing values generated)

.                         replace ethfrac  = e 
(157 real changes made, 155 to missing)

.                         drop e

.                         tsset cow year
       panel variable:  cowcode (unbalanced)
        time variable:  year, 1946 to 2010
                delta:  1 year

.                         gen excluded = l.exclpop 
(172 missing values generated)

.                         gen  loggdp = ln(gdpcapl)
(131 missing values generated)

.                         gen logoil = ln(1+oilpcl)
(97 missing values generated)

.                         tsset cow year
       panel variable:  cowcode (unbalanced)
        time variable:  year, 1946 to 2010
                delta:  1 year

.                         gen gr= d.loggdp
(289 missing values generated)

.                         tssmooth ma  grow = gr, window(2 0 0)
The smoother applied was
     by cowcode : (1/2)*[x(t-2) + x(t-1) + 0*x(t)]; x(t)= gr

.                         replace grow =gr if grow==. & gr~=.
(158 real changes made)

.                         sort cow year

.                         saveold epr-merge,replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in older Stata formats)
file epr-merge.dta saved

.                         
.                 * NAVCO data *  /* downloaded 3.8.2018 */
.                         use navco2-original, clear 

.                         sum year  /* note that this ends in 2006!!!*/

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        year |      1,726    1980.882    14.97577       1945       2006

.                         gen violent = 1 if prim_method==0
(345 missing values generated)

.                         gen nonvio = 1 if prim_method==1
(1,381 missing values generated)

.                         gen regch = 1 if camp_goal==0
(1,012 missing values generated)

.                         gen regchnv = camp_goal==0 & nonvio==1

.                         gen regchv = camp_goal==0 & violent==1

.                         gen cowcode = lccode

.                         recode cow (347=345) (364=365)        /* Russia/USSR get same cowcode;
>  Yugoslavia */
(cowcode: 23 changes made)

.                         recode cow (531=530) if year<1990     /* Eritrea coded as Ethiopia pri
> or to 1990 */
(cowcode: 8 changes made)

.                         local var = "violent nonvio regch regchnv regchv"

.                         foreach v of local var {
  2.                                 egen nav_`v' = max(`v'), by(cow year)
  3.                         }
(294 missing values generated)
(1318 missing values generated)
(922 missing values generated)

.                         egen tag = tag(cow year)

.                         keep if tag
(213 observations deleted)

.                         drop tag

.                         keep cow year nav_* location

.                         sort cow year

.                         merge cow year using gwf-original
(note: you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge        /* note that GWF consider pre-1955 Vietnam as "not i
> ndependent" */

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        655       12.49       12.49
          2 |      3,733       71.16       83.64
          3 |        858       16.36      100.00
------------+-----------------------------------
      Total |      5,246      100.00

.                         recode nav_* (.=0)  if year<=2006
(nav_violent: 3787 changes made)
(nav_nonvio: 4677 changes made)
(nav_regch: 4312 changes made)
(nav_regchnv: 3500 changes made)
(nav_regchv: 3500 changes made)

.                         tsset cow year
       panel variable:  cowcode (unbalanced)
        time variable:  year, 1945 to 2010, but with gaps
                delta:  1 unit

.                         gen nav_protest = l.nav_viol==1 | l.nav_nonvio==1  if l.nav_nonvio~=. 
(380 missing values generated)

.                         gen nav_protest_regch = l.nav_regch==1 if l.nav_nonvio~=. 
(380 missing values generated)

.                         gen nav_protestNV = l.nav_nonvio==1   if l.nav_nonvio~=.
(380 missing values generated)

.                         gen nav_protestV = l.nav_vio==1   if l.nav_nonvio~=.
(380 missing values generated)

.                         rename _merge merge4

.                         drop if merge4 ==1
(655 observations deleted)

.                         drop merge4

.                         rename location nav_country

.                         sort cow year

.                         saveold navco-merge, replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in older Stata formats)
file navco-merge.dta saved

.                         
.                 * Fariss data *   /* downloaded 3.8.2018 */
.                         use fariss-original,clear

.                         recode cow (679=678) if year>1990
(cowcode: 20 changes made)

.                         local var="ciri disap kill polpris tort amnesty state hathaway itt gen
> ocide rummel massive_repression executions killing additive"

.                         foreach v of local var {
  2.                                 replace `v' = "." if `v'=="NA"
  3.                                 destring `v', replace
  4.                         }
(4,530 real changes made)
ciri: all characters numeric; replaced as int
(4530 missing values generated)
(4,541 real changes made)
disap: all characters numeric; replaced as byte
(4541 missing values generated)
(4,543 real changes made)
kill: all characters numeric; replaced as byte
(4543 missing values generated)
(4,536 real changes made)
polpris: all characters numeric; replaced as byte
(4536 missing values generated)
(4,537 real changes made)
tort: all characters numeric; replaced as byte
(4537 missing values generated)
(4,578 real changes made)
amnesty: all characters numeric; replaced as byte
(4578 missing values generated)
(3,619 real changes made)
state: all characters numeric; replaced as byte
(3619 missing values generated)
(7,035 real changes made)
hathaway: all characters numeric; replaced as byte
(7035 missing values generated)
(7,809 real changes made)
itt: all characters numeric; replaced as byte
(7809 missing values generated)
(599 real changes made)
genocide: all characters numeric; replaced as byte
(599 missing values generated)
(4,366 real changes made)
rummel: all characters numeric; replaced as byte
(4366 missing values generated)
(4,197 real changes made)
massive_repression: all characters numeric; replaced as byte
(4197 missing values generated)
(5,304 real changes made)
executions: all characters numeric; replaced as byte
(5304 missing values generated)
(5,122 real changes made)
killing: all characters numeric; replaced as byte
(5122 missing values generated)
(4,559 real changes made)
additive: all characters numeric; replaced as byte
(4559 missing values generated)

.                         sort cow year

.                         saveold fariss-merge,replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in older Stata formats)
file fariss-merge.dta saved

. 
.                 * Personalism data *
.                         use gwf-original,clear

.                         
.                         * Generate binary variables *
.                         gen milmerit_persB = milmerit_pers

.                         recode milmerit_persB (2=1) (1=0)  
(milmerit_persB: 3502 changes made)

.                         tsset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1946 to 2010
                delta:  1 unit

.                         gen newparty =support==1 & l.support==0

.                         gen yr = year if newparty==1
(4,519 missing values generated)

.                         egen yrs = max(yr), by(gwf_leaderid)
(3609 missing values generated)

.                         tsset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1946 to 2010
                delta:  1 unit

.                         replace newparty=1 if l.newparty==1 & l.gwf_leaderid==gwf_leaderid & y
> ear==year[_n-1]+1
(659 real changes made)

.                         gen createparty =militparty_new==1 | (newparty==1  & partyhistory_post
> ==1) 

.                         
.                         * Label variables *
.                         global pvars1 = "partyexcom_pers partyrbr officepers createparty"

.                         global pvars2 = "milnotrial milmerit_persB paramil_pers sectyapp_pers"

.                         label var officepers "Appointments to high office"

.                         label var createparty "Create new party"

.                         label var partyexcom_pers "Party exec committee"

.                         label var partyrbr "Rubber stamp party"

.                         label var milmerit_persB "Military promotions"

.                         label var milnotrial "Military purge"

.                         label var sectyapp_pers "Security apparatus"

.                         label var paramil_pers "Paramilitary"

.                         set seed 2453456

.                         
.                         * IRT model *
.                         irt 2pl $pvars1 $pvars2 

Fitting fixed-effects model:

Iteration 0:   log likelihood = -22969.605  
Iteration 1:   log likelihood =  -22931.47  
Iteration 2:   log likelihood = -22931.455  
Iteration 3:   log likelihood = -22931.455  

Fitting full model:

Iteration 0:   log likelihood = -21492.543  
Iteration 1:   log likelihood = -20095.733  
Iteration 2:   log likelihood = -20026.085  
Iteration 3:   log likelihood = -19907.278  
Iteration 4:   log likelihood = -19904.262  
Iteration 5:   log likelihood = -19904.256  
Iteration 6:   log likelihood = -19904.256  

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -19904.256
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
partyexcom~s |
     Discrim |   2.131928   .1134983    18.78   0.000     1.909476    2.354381
        Diff |   .6085253   .0280052    21.73   0.000      .553636    .6634145
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   2.004324   .1048153    19.12   0.000      1.79889    2.209759
        Diff |   .6785336   .0298494    22.73   0.000     .6200299    .7370373
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.890977   .1530346    18.89   0.000     2.591034    3.190919
        Diff |  -.4202195   .0232182   -18.10   0.000    -.4657263   -.3747127
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.283182   .0690587    18.58   0.000      1.14783    1.418535
        Diff |   1.644761   .0679592    24.20   0.000     1.511564    1.777959
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.558655    .070848    22.00   0.000     1.419795    1.697514
        Diff |   .5093505   .0305189    16.69   0.000     .4495346    .5691664
-------------+----------------------------------------------------------------
milmerit_p~B |
     Discrim |   1.366218   .0621683    21.98   0.000     1.244371    1.488066
        Diff |   .3094893   .0305383    10.13   0.000     .2496355    .3693432
-------------+----------------------------------------------------------------
paramil_pers |
     Discrim |   1.111254   .0554924    20.03   0.000     1.002491    1.220017
        Diff |   .6777748   .0411037    16.49   0.000     .5972129    .7583367
-------------+----------------------------------------------------------------
sectyapp_p~s |
     Discrim |   1.760407   .0783432    22.47   0.000     1.606858    1.913957
        Diff |   -.332487   .0270983   -12.27   0.000    -.3855987   -.2793753
------------------------------------------------------------------------------

.                         estat report $pvars1 $pvars2, byparm sort(b)

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -19904.256
---------------------------------------------------------------------------------
                |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
Discrim         |
     officepers |   2.890977   .1530346    18.89   0.000     2.591034    3.190919
  sectyapp_pers |   1.760407   .0783432    22.47   0.000     1.606858    1.913957
 milmerit_persB |   1.366218   .0621683    21.98   0.000     1.244371    1.488066
     milnotrial |   1.558655    .070848    22.00   0.000     1.419795    1.697514
partyexcom_pers |   2.131928   .1134983    18.78   0.000     1.909476    2.354381
   paramil_pers |   1.111254   .0554924    20.03   0.000     1.002491    1.220017
   partyrbrstmp |   2.004324   .1048153    19.12   0.000      1.79889    2.209759
    createparty |   1.283182   .0690587    18.58   0.000      1.14783    1.418535
----------------+----------------------------------------------------------------
Diff            |
     officepers |  -.4202195   .0232182   -18.10   0.000    -.4657263   -.3747127
  sectyapp_pers |   -.332487   .0270983   -12.27   0.000    -.3855987   -.2793753
 milmerit_persB |   .3094893   .0305383    10.13   0.000     .2496355    .3693432
     milnotrial |   .5093505   .0305189    16.69   0.000     .4495346    .5691664
partyexcom_pers |   .6085253   .0280052    21.73   0.000      .553636    .6634145
   paramil_pers |   .6777748   .0411037    16.49   0.000     .5972129    .7583367
   partyrbrstmp |   .6785336   .0298494    22.73   0.000     .6200299    .7370373
    createparty |   1.644761   .0679592    24.20   0.000     1.511564    1.777959
---------------------------------------------------------------------------------

.                         predict pers_2pl, latent se(pers_se_2pl)
(option ebmeans assumed)
(using 7 quadrature points)

.                         
.                         * IRT plots *
.                         irtgraph iif  (sectyapp_pers,lcolor(blue)) (milmerit_pers,lcolor(red))
>  (milnotrial,lcolor(green)) ///
>                         (paramil_pers,lcolor(cyan)),legend(col(2) pos(6)) title(Security & mil
> itary items) saving(t2,replace) ///
>                         ylab(,glcolor(gs15)) xtitle("Personalism ({&theta})")
(file t2.gph saved)

.                         irtgraph iif  (officepers,lcolor(blue)) (partyexcom_pers,lcolor(red)) 
> (partyrbr,lcolor(green)) ///
>                         (createparty,lcolor(cyan)),legend(col(2) pos(6))  title(Party & person
> nel items) saving(t1,replace) ///
>                         ylab(,glcolor(gs15)) xtitle("Personalism ({&theta})")
(file t1.gph saved)

.                         gr combine t1.gph t2.gph, col(2)   ysize(5.5) xsize(9)  ycommon

.                         erase t1.gph

.                         erase t2.gph

.                         
.                         * IRT-7 model *
.                         irt 2pl $pvars1 milmerit_persB paramil_pers sectyapp_pers

Fitting fixed-effects model:

Iteration 0:   log likelihood = -19949.987  
Iteration 1:   log likelihood = -19917.404  
Iteration 2:   log likelihood =  -19917.39  
Iteration 3:   log likelihood =  -19917.39  

Fitting full model:

Iteration 0:   log likelihood =   -18755.7  
Iteration 1:   log likelihood = -17628.501  
Iteration 2:   log likelihood =  -17559.28  
Iteration 3:   log likelihood = -17416.858  
Iteration 4:   log likelihood = -17411.816  
Iteration 5:   log likelihood = -17411.795  
Iteration 6:   log likelihood = -17411.795  

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -17411.795
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
partyexcom~s |
     Discrim |   2.790729   .1798829    15.51   0.000     2.438165    3.143293
        Diff |   .5619797   .0250379    22.45   0.000     .5129064     .611053
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   2.723998   .1734306    15.71   0.000      2.38408    3.063916
        Diff |    .614656   .0259507    23.69   0.000     .5637935    .6655185
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   3.036054   .1853052    16.38   0.000     2.672863    3.399245
        Diff |  -.4097298   .0232728   -17.61   0.000    -.4553436    -.364116
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.135807   .0652371    17.41   0.000     1.007944    1.263669
        Diff |   1.779118   .0812062    21.91   0.000     1.619956    1.938279
-------------+----------------------------------------------------------------
milmerit_p~B |
     Discrim |   1.046155   .0518082    20.19   0.000     .9446125    1.147697
        Diff |   .3670575   .0369129     9.94   0.000     .2947096    .4394054
-------------+----------------------------------------------------------------
paramil_pers |
     Discrim |   .9584773   .0527428    18.17   0.000     .8551034    1.061851
        Diff |     .75188    .048178    15.61   0.000     .6574528    .8463072
-------------+----------------------------------------------------------------
sectyapp_p~s |
     Discrim |    1.54347   .0712759    21.65   0.000     1.403772    1.683168
        Diff |  -.3526959   .0293814   -12.00   0.000    -.4102824   -.2951093
------------------------------------------------------------------------------

.                         estat report $pvars1 milmerit_persB paramil_pers sectyapp_pers, byparm
>  sort(b)

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -17411.795
---------------------------------------------------------------------------------
                |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
Discrim         |
     officepers |   3.036054   .1853052    16.38   0.000     2.672863    3.399245
  sectyapp_pers |    1.54347   .0712759    21.65   0.000     1.403772    1.683168
 milmerit_persB |   1.046155   .0518082    20.19   0.000     .9446125    1.147697
partyexcom_pers |   2.790729   .1798829    15.51   0.000     2.438165    3.143293
   partyrbrstmp |   2.723998   .1734306    15.71   0.000      2.38408    3.063916
   paramil_pers |   .9584773   .0527428    18.17   0.000     .8551034    1.061851
    createparty |   1.135807   .0652371    17.41   0.000     1.007944    1.263669
----------------+----------------------------------------------------------------
Diff            |
     officepers |  -.4097298   .0232728   -17.61   0.000    -.4553436    -.364116
  sectyapp_pers |  -.3526959   .0293814   -12.00   0.000    -.4102824   -.2951093
 milmerit_persB |   .3670575   .0369129     9.94   0.000     .2947096    .4394054
partyexcom_pers |   .5619797   .0250379    22.45   0.000     .5129064     .611053
   partyrbrstmp |    .614656   .0259507    23.69   0.000     .5637935    .6655185
   paramil_pers |     .75188    .048178    15.61   0.000     .6574528    .8463072
    createparty |   1.779118   .0812062    21.91   0.000     1.619956    1.938279
---------------------------------------------------------------------------------

.                         predict pers7_2pl, latent se(pers7_se_2pl)
(option ebmeans assumed)
(using 7 quadrature points)

.                         
.                         * Standardize, rescale *
.                         qui sum pers_2pl

.                         gen latent_personalism = (pers_2pl+abs(r(min))) / (r(max) - r(min))

.                         hist latent, bin(50)
(bin=50, start=0, width=.02)

.                         qui sum pers7_2pl

.                         gen latent7_personalism = (pers7_2pl+abs(r(min))) / (r(max) - r(min))

.                         
.                         * Variance decomposition *
.                         qui xtset gwf_leaderid year

.                         qui xtsum `i'

.                         qui scalar sdb = r(sd_b)

.                         qui scalar sdw = r(sd_w)

.                         qui scalar vart= sdb + sdw

.                         qui scalar varr = sdw / vart

.                         scalar list sdw
       sdw =  .12685133

.                         scalar list varr
      varr =  .32712836

.                         sort cow year

.                         save temp-pers,replace
file temp-pers.dta saved

.                         
.                 * Merge data sets *
.                         use temp-pers,clear

.                         merge cow year using fariss-merge
(note: you are using old merge syntax; see [D] merge for new syntax)
(note: variable cowcode was int, now float to accommodate using data's values)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        122        1.30        1.30
          2 |      4,799       51.11       52.41
          3 |      4,469       47.59      100.00
------------+-----------------------------------
      Total |      9,390      100.00

.                         drop if _merge==2
(4,799 observations deleted)

.                         tab year if _merge==1  /* note that 1946-1948 are not in the Fariss da
> ta */

       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1946 |         32       26.23       26.23
       1947 |         32       26.23       52.46
       1948 |         32       26.23       78.69
       1949 |          1        0.82       79.51
       1950 |          2        1.64       81.15
       1951 |          2        1.64       82.79
       1952 |          2        1.64       84.43
       1953 |          2        1.64       86.07
       1954 |          1        0.82       86.89
       1955 |          1        0.82       87.70
       1956 |          1        0.82       88.52
       1957 |          1        0.82       89.34
       1958 |          1        0.82       90.16
       1959 |          1        0.82       90.98
       1960 |          1        0.82       91.80
       1961 |          1        0.82       92.62
       1962 |          1        0.82       93.44
       1963 |          1        0.82       94.26
       1964 |          1        0.82       95.08
       1965 |          1        0.82       95.90
       1966 |          1        0.82       96.72
       1967 |          1        0.82       97.54
       1968 |          1        0.82       98.36
       1969 |          1        0.82       99.18
       1970 |          1        0.82      100.00
------------+-----------------------------------
      Total |        122      100.00

.                         rename _merge merge1

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using navco-merge
(note: you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      4,591      100.00      100.00
------------+-----------------------------------
      Total |      4,591      100.00

.                         drop if _merge==2
(0 observations deleted)

.                         rename _merge merge2

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using epr-merge
(note: you are using old merge syntax; see [D] merge for new syntax)
(note: variable country was str15, now str32 to accommodate using data's values)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         76        0.95        0.95
          2 |      3,393       42.50       43.45
          3 |      4,515       56.55      100.00
------------+-----------------------------------
      Total |      7,984      100.00

.                         tab gwf_country if _merge==1  /* note that Singapore and Oman are not 
> in EPR data */

          Country name |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
          Germany East |          5        6.58        6.58
                  Oman |         25       32.89       39.47
             Singapore |         45       59.21       98.68
           South Yemen |          1        1.32      100.00
-----------------------+-----------------------------------
                 Total |         76      100.00

.                         drop if _merge==2
(3,393 observations deleted)

.                         rename _merge merge3

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using coups-merge
(note: you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      4,321       91.33       91.33
          2 |        140        2.96       94.29
          3 |        270        5.71      100.00
------------+-----------------------------------
      Total |      4,731      100.00

.                         tab year if _merge==2  /* note that coup data begin in 1950 */

       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1950 |          1        0.71        0.71
       1951 |          1        0.71        1.43
       1952 |          2        1.43        2.86
       1954 |          2        1.43        4.29
       1955 |          2        1.43        5.71
       1956 |          1        0.71        6.43
       1957 |          1        0.71        7.14
       1958 |          2        1.43        8.57
       1959 |          2        1.43       10.00
       1960 |          2        1.43       11.43
       1961 |          7        5.00       16.43
       1962 |          5        3.57       20.00
       1963 |          5        3.57       23.57
       1964 |          2        1.43       25.00
       1965 |          1        0.71       25.71
       1966 |          4        2.86       28.57
       1967 |          2        1.43       30.00
       1968 |          2        1.43       31.43
       1969 |          3        2.14       33.57
       1971 |          1        0.71       34.29
       1972 |          4        2.86       37.14
       1973 |          3        2.14       39.29
       1974 |          1        0.71       40.00
       1975 |          4        2.86       42.86
       1976 |          3        2.14       45.00
       1977 |          1        0.71       45.71
       1978 |          1        0.71       46.43
       1979 |          2        1.43       47.86
       1980 |          4        2.86       50.71
       1981 |          3        2.14       52.86
       1983 |          2        1.43       54.29
       1984 |          1        0.71       55.00
       1985 |          1        0.71       55.71
       1986 |          1        0.71       56.43
       1987 |          3        2.14       58.57
       1988 |          1        0.71       59.29
       1989 |          3        2.14       61.43
       1990 |          2        1.43       62.86
       1991 |          3        2.14       65.00
       1992 |          3        2.14       67.14
       1994 |          2        1.43       68.57
       1995 |          2        1.43       70.00
       1996 |          4        2.86       72.86
       1997 |          1        0.71       73.57
       1999 |          3        2.14       75.71
       2000 |          6        4.29       80.00
       2001 |          1        0.71       80.71
       2002 |          1        0.71       81.43
       2003 |          2        1.43       82.86
       2006 |          2        1.43       84.29
       2008 |          2        1.43       85.71
       2009 |          2        1.43       87.14
       2010 |          2        1.43       88.57
       2011 |          2        1.43       90.00
       2012 |          5        3.57       93.57
       2013 |          1        0.71       94.29
       2014 |          4        2.86       97.14
       2015 |          2        1.43       98.57
       2016 |          1        0.71       99.29
       2017 |          1        0.71      100.00
------------+-----------------------------------
      Total |        140      100.00

.                         drop if _merge==2
(140 observations deleted)

.                         rename _merge merge4

.                         recode coup* (.=0) if year>=1950 & year<=2010
(coup: 4187 changes made)
(coupA: 4187 changes made)
(coupS: 4187 changes made)

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using nelda-merge
(note: you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      3,681       64.34       64.34
          2 |      1,130       19.75       84.09
          3 |        910       15.91      100.00
------------+-----------------------------------
      Total |      5,721      100.00

.                         drop if _merge==2
(1,130 observations deleted)

.                         rename _merge merge5

.                         rename nelda_country Nelda_country

.                         recode nelda* (.=0) if year>=1945 & year<=2010
(nelda_election: 3681 changes made)
(nelda_relection: 3681 changes made)
(nelda_irelection: 3681 changes made)
(nelda_mparty: 3681 changes made)
(nelda_incumb: 3681 changes made)
(nelda_boycott: 3681 changes made)
(nelda_maxmonth: 3681 changes made)
(nelda_minmonth: 3681 changes made)
(nelda_1: 3681 changes made)
(nelda_2: 3681 changes made)
(nelda_3: 3681 changes made)
(nelda_4: 3681 changes made)
(nelda_5: 3681 changes made)
(nelda_6: 3681 changes made)
(nelda_7: 3681 changes made)
(nelda_8: 3681 changes made)
(nelda_9: 3681 changes made)
(nelda_10: 3681 changes made)
(nelda_11: 3681 changes made)
(nelda_12: 3681 changes made)
(nelda_13: 3681 changes made)
(nelda_14: 3681 changes made)
(nelda_15: 3681 changes made)
(nelda_16: 3681 changes made)
(nelda_17: 3681 changes made)
(nelda_18: 3681 changes made)
(nelda_19: 3681 changes made)
(nelda_20: 3681 changes made)
(nelda_21: 3681 changes made)
(nelda_22: 3681 changes made)
(nelda_23: 3681 changes made)
(nelda_24: 3681 changes made)
(nelda_25: 3681 changes made)
(nelda_26: 3681 changes made)
(nelda_27: 3681 changes made)
(nelda_28: 3681 changes made)
(nelda_29: 3681 changes made)
(nelda_30: 3681 changes made)
(nelda_31: 3681 changes made)
(nelda_32: 3681 changes made)
(nelda_33: 3681 changes made)
(nelda_34: 3681 changes made)
(nelda_35: 3681 changes made)
(nelda_36: 3681 changes made)
(nelda_37: 3681 changes made)
(nelda_38: 3681 changes made)
(nelda_39: 3681 changes made)
(nelda_40: 3681 changes made)
(nelda_41: 3681 changes made)
(nelda_42: 3681 changes made)
(nelda_43: 3681 changes made)
(nelda_44: 3681 changes made)
(nelda_45: 3681 changes made)
(nelda_46: 3681 changes made)
(nelda_47: 3681 changes made)
(nelda_48: 3681 changes made)
(nelda_49: 3681 changes made)
(nelda_50: 3681 changes made)
(nelda_51: 3681 changes made)
(nelda_52: 3681 changes made)
(nelda_53: 3681 changes made)
(nelda_54: 3681 changes made)
(nelda_55: 3681 changes made)
(nelda_56: 3681 changes made)
(nelda_57: 3681 changes made)
(nelda_58: 3681 changes made)

.                         rename Nelda_country nelda_country

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using prio-mergeB
(note: you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      3,646       68.51       68.51
          2 |        731       13.74       82.24
          3 |        945       17.76      100.00
------------+-----------------------------------
      Total |      5,322      100.00

.                         drop if _merge==2
(731 observations deleted)

.                         rename _merge merge6

.                         rename prio_country Prio_country

.                         recode prio* (.=0) if year>=1945 & year<=2010
(prio_conflict_intra: 3646 changes made)
(prio_conflict_inter: 3646 changes made)
(prio_conflict_duration_intra: 3651 changes made)
(prio_conflict_duration_inter: 3651 changes made)
(prio_conflict_cumint_intra: 3649 changes made)
(prio_conflict_cumint_inter: 3648 changes made)
(prio_conflict_int_intra: 3649 changes made)
(prio_conflict_int_inter: 3648 changes made)
(prio_lconflict_int_intra: 3651 changes made)
(prio_lconflict_int_inter: 3651 changes made)
(prio_lconflict_intra: 3651 changes made)
(prio_lconflict_inter: 3651 changes made)
(prio_lconflict_duration_intra: 3651 changes made)
(prio_lconflict_duration_inter: 3651 changes made)
(prio_lconflict_cumint_intra: 3651 changes made)
(prio_lconflict_cumint_inter: 3651 changes made)

.                         rename Prio_country prio_country

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using nmc-merge
(note: you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      4,591      100.00      100.00
------------+-----------------------------------
      Total |      4,591      100.00

.                         drop if _merge==2
(0 observations deleted)

.                         rename _merge merge7

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using GWFtscs
(note: you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      4,591      100.00      100.00
------------+-----------------------------------
      Total |      4,591      100.00

.                         rename _merge merge8

.                         sort cow year

.                         merge cow year using urdal-merge
(note: you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      4,591      100.00      100.00
------------+-----------------------------------
      Total |      4,591      100.00

.                         rename _merge merge9

.                         sort cow  year

.                         merge cow year using latent-protest-data
(note: you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        397        4.57        4.57
          2 |      4,089       47.11       51.68
          3 |      4,194       48.32      100.00
------------+-----------------------------------
      Total |      8,680      100.00

.                         rename _merge merge10

.                         sort cow year

.                         merge cow year using ji-merge
(note: you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge   

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        203        2.03        2.03
          2 |      1,326       13.25       15.28
          3 |      8,477       84.72      100.00
------------+-----------------------------------
      Total |     10,006      100.00

.                         tab gwf_country if _merge==1  /* no JI data for South Vietnam */

          Country name |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
           Afghanistan |          2        2.22        2.22
               Albania |          2        2.22        4.44
             Argentina |          1        1.11        5.56
               Bolivia |          2        2.22        7.78
              Bulgaria |          2        2.22       10.00
              Cambodia |          1        1.11       11.11
           Congo/Zaire |          2        2.22       13.33
         Dominican Rep |          2        2.22       15.56
               Ecuador |          2        2.22       17.78
                 Egypt |          2        2.22       20.00
           El Salvador |          2        2.22       22.22
              Ethiopia |          2        2.22       24.44
          Germany East |          1        1.11       25.56
                 Haiti |          1        1.11       26.67
              Honduras |          2        2.22       28.89
                  Iran |          2        2.22       31.11
                  Iraq |          2        2.22       33.33
                Jordan |          1        1.11       34.44
               Liberia |          2        2.22       36.67
                Mexico |          2        2.22       38.89
              Mongolia |          2        2.22       41.11
                 Nepal |          2        2.22       43.33
             Nicaragua |          2        2.22       45.56
                  Oman |          2        2.22       47.78
              Paraguay |          2        2.22       50.00
                Poland |          2        2.22       52.22
              Portugal |          2        2.22       54.44
               Romania |          2        2.22       56.67
          Saudi Arabia |          2        2.22       58.89
          South Africa |          2        2.22       61.11
         South Vietnam |         21       23.33       84.44
          Soviet Union |          2        2.22       86.67
                 Spain |          2        2.22       88.89
                 Syria |          1        1.11       90.00
              Thailand |          2        2.22       92.22
               Tunisia |          1        1.11       93.33
                Turkey |          2        2.22       95.56
                 Yemen |          2        2.22       97.78
            Yugoslavia |          2        2.22      100.00
-----------------------+-----------------------------------
                 Total |         90      100.00

.                         rename _merge merge11

.                         keep if gwf_caseid~=.
(5,415 observations deleted)

.                         saveold temp,replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in older Stata formats)
file temp.dta saved

.  
.                 
. 
.                 * Variable transformations *
.                 gen seniorofficer = militrank>=4 & leadermil==1

.                 gen juniorofficer = militrank>=1 & militrank<=3 & leadermil==1

.                 qui sum latentmean 

.                 gen repression = (latentmean - r(mean))/r(sd)  /*standardize within sample */
(122 missing values generated)

.                 replace repression = repression*-1   /* flip scale */
(4,469 real changes made)

.                 qui sum lpopl

.                 replace lpopl = (lpopl - r(mean))/r(sd)  /*standardize within sample */
(4,510 real changes made)

.                 qui sum loggdp

.                 replace loggdp = (loggdp - r(mean))/r(sd)  /*standardize within sample */
(4,485 real changes made)

.                 qui sum G_age

.                 replace G_age = (G_age - r(mean))/r(sd)  /*standardize within sample */
variable G_age was byte now float
(4,578 real changes made)

.                 gen lt = ln(gwf_leader_duration)

.                 qui sum lt

.                 replace lt = (lt - r(mean))/r(sd)  /*standardize within sample */
(4,591 real changes made)

.                 gen civwar = prio_lconflict_intra>0  & prio_lconflict_int_intra==2

.                 gen intwar = prio_lconflict_inter>0  & prio_lconflict_int_inter==2

.                 xtset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1946 to 2010
                delta:  1 unit

.                 gen mpartyelec = nelda_mparty==1 | l.nelda_mparty==1 | f.nelda_mparty==1

.                 gen coup012 =  coupA==1 | l1.coupA==1 | l2.coupA==1  | coupS==1 | l1.coupS==1 
> | l2.coupS==1  

.                 gen inheritparty = (partyhistory_priorwonsupport==1 | partyhistory_priorno | /
> *
>                                 */ partyhistory_insurgent==1 | partyhistory_priordem==1)  if g
> wf_case_duration==1 | year==1946
(4,311 missing values generated)

.                 egen inh= max(inheritparty),by(gwf_caseid)   /* ensure no within case variatio
> n */

.                 replace inherit = inh
(4,311 real changes made)

.                 drop inh

.                 tab gwf_prior

Regime type of the country |
   prior to regime seizing |
                     power |      Freq.     Percent        Cum.
---------------------------+-----------------------------------
                 democracy |        875       19.06       19.06
          dictatorship_mil |        372        8.10       27.16
       dictatorship_nonmil |      1,286       28.01       55.17
          foreign-occupied |        405        8.82       63.99
           not-independent |      1,494       32.54       96.54
                   warlord |        159        3.46      100.00
---------------------------+-----------------------------------
                     Total |      4,591      100.00

.                 gen priordem = gwf_prior=="democracy"

.                 gen priornondem_mil = gwf_prior=="dictatorship_mil"

.                 gen priornondem_nonmil = gwf_prior=="dictatorship_nonmil"

.                 gen firstlow = gwf_firstldr==1 & ldr_exp_lowrank==1

.                 egen maxfirstlow = max(firstlow), by(gwf_caseid)

.                 gen divided = seizure_uprising==1 | (seizure_coup==1 & maxfirstlow==1)

.                 drop maxfirstlow

.                 rename latent_pers xpers

.                 gen institutions = (support*legcomp)/8  /* 0-1 scale */

.                 * Obtain estimating sample *
.                 qui reg latentmean  xpers

.                 keep if e(sample)==1 & year>=1950  &year<=2010
(159 observations deleted)

.                 * Save data *
.                 sort cow year

.                 saveold temp, replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in older Stata formats)
file temp.dta saved

. 
. ********************
. **** China data ****
. ********************
.                 use temp,clear

.                 list year repression xpers if gwf_country=="China" & (year==1989 | year==2000 
> | year==1968), clean noobs

    year   repres~n      xpers  
    1968   2.477836   .8719397  
    1989   1.234869          0  
    2000   .7646369          0  

.                 twoway (hist xpers, bin(35) freq   title("Personalism distribution"))  ///
>                 (pcarrowi 240 .75 170 .87 ,color(red)saving(t1.gph,replace) legend(off) text(2
> 45 .75  "China" "1968" , place(t))) ///
>                 (pcarrowi 250 .12 180 0.01 ,color(red) ytitle(Frequency) xtitle(Personalism) l
> egend(off) text(255 .12 "China" "post-Deng", place(t)))
(note: file t1.gph not found)
(file t1.gph saved)

.                 twoway (hist repression, bin(35) freq saving(t2.gph, replace)  ytitle(Frequenc
> y) xtitle(Repression)  title(Repression distribution)) ///
>                  (pcarrowi 150 1.45 130 .75 ,color(red) legend(off) text(154 1.45 "China 2000"
> , place(e))) ///
>                  (pcarrowi 128.5 1.91 112.5 1.31 ,color(red) legend(off) text(128.5 1.91 "Chin
> a 1989", place(e)))
(note: file t2.gph not found)
(file t2.gph saved)

.                 gr combine t1.gph t2.gph,col(2) ysize(3) title("Dictatorships, 1950-2010")

.                 graph export "$dir\pers-repression-distributions-with-China.pdf", as(pdf) repl
> ace       
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\pers-repression-dist
> ributions-with-China.pdf written in PDF format)

.                 twoway (hist xpers, bin(35) freq   title("Personalism distribution"))  ///
>                 (pcarrowi 240 .75 170 .87 ,color(red)saving(t1.gph,replace) legend(off) text(2
> 45 .75  "China" "1968" , place(t))) ///
>                 (pcarrowi 250 .12 180 0.01 ,color(red) ytitle(Frequency) xtitle(Personalism) l
> egend(off) text(255 .12 "China" "post-Deng", place(t)))
(file t1.gph saved)

.                 twoway (hist repression, bin(35) freq saving(t2.gph, replace)  ytitle(Frequenc
> y) xtitle(Repression) ///
>                 title(Repression distribution))  
(file t2.gph saved)

.                 twoway (hist xpers, bin(35) freq saving(t1.gph,replace) xtitle(Personalism) ti
> tle("Personalism distribution"))  
(file t1.gph saved)

.                 gr combine  t2.gph t1.gph,col(2) ysize(3) title("Dictatorships, 1950-2010")

.                 graph export "$dir\pers-repression-distributions.pdf", as(pdf) replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\pers-repression-dist
> ributions.pdf written in PDF format)

.                 
. **********************************************************************
. **** Show that personalism index is higher in personalist regimes ****
. **********************************************************************
. use temp,clear

. table gwf_regimetype, c(mean xpers median xpers)

--------------------------------------------------
Regime type, including  |
hybrids (10)            | mean(xpers)   med(xpers)
------------------------+-------------------------
      indirect military |    .0428233            0
               military |    .1757881     .1362545
      military-personal |    .3983451     .4225851
               monarchy |    .4195496     .4196175
              oligarchy |    .0475957            0
            party-based |    .2836183     .2492389
         party-military |    .4066935     .4270882
         party-personal |     .584939     .5673903
party-personal-military |     .628364     .6185502
               personal |    .6108823     .6097388
--------------------------------------------------

. local var = "gwf_pers gwf_party gwf_mon gwf_mil"

. foreach v of local var {
  2.         ttest xpers, by(`v')
  3. }

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   3,306     .358891    .0045036    .2589463    .3500609    .3677211
       1 |   1,126    .6108823    .0071713    .2406387    .5968117    .6249529
---------+--------------------------------------------------------------------
combined |   4,432    .4229123    .0041615    .2770428    .4147537    .4310708
---------+--------------------------------------------------------------------
    diff |           -.2519913    .0087788               -.2692021   -.2347805
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t = -28.7046
Ho: diff = 0                                     degrees of freedom =     4430

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,232    .4761377    .0057828    .2732032    .4647975    .4874779
       1 |   2,200    .3689126    .0057658    .2704409    .3576056    .3802197
---------+--------------------------------------------------------------------
combined |   4,432    .4229123    .0041615    .2770428    .4147537    .4310708
---------+--------------------------------------------------------------------
    diff |            .1072251    .0081667                .0912142    .1232359
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  13.1295
Ho: diff = 0                                     degrees of freedom =     4430

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   3,897    .4233739    .0046527    .2904509    .4142519    .4324959
       1 |     535    .4195496    .0063199    .1461807    .4071346    .4319646
---------+--------------------------------------------------------------------
combined |   4,432    .4229123    .0041615    .2770428    .4147537    .4310708
---------+--------------------------------------------------------------------
    diff |            .0038244    .0127747               -.0212203    .0288691
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.2994
Ho: diff = 0                                     degrees of freedom =     4430

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6177         Pr(|T| > |t|) = 0.7647          Pr(T > t) = 0.3823

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   3,861    .4464958    .0043456    .2700249    .4379758    .4550158
       1 |     571    .2634446    .0113567    .2713755    .2411385    .2857507
---------+--------------------------------------------------------------------
combined |   4,432    .4229123    .0041615    .2770428    .4147537    .4310708
---------+--------------------------------------------------------------------
    diff |            .1830512    .0121148                .1593002    .2068022
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  15.1097
Ho: diff = 0                                     degrees of freedom =     4430

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. * .61 (personalist), .42 (dominant party), .42 (monarchy), .26 (military junta)
. 
. 
.  
.                 
. ******************
. **** Analysis ****
. ******************
.                 use temp,clear

.                 keep if year>=1955
(218 observations deleted)

.                 * rescale vars *
.                 local var = "ythblgap  loggdp lpopl mean5"

.                 foreach v of local var {
  2.                         qui: sum `v'
  3.                         qui replace `v'= (`v'-r(mean))/r(sd)
  4.                 }

.                 global leadervar="seniorofficer juniorofficer"

.                 global conflictvar = "civwar intwar mean5"

.                 label var repress "Repression"

.                 label var xpers "Personalism"

.                 label var senior "Senior officer"

.                 label var junior "Junior officer"

.                 label var civwar "Civil conflict"

.                 label var intwar "Int'l conflict"

.                 label var mean5 "Protest"

.                 label var ld "Regime duration"

.                 label var lt "Leader time in power (log)"

.                 label var loggdp "GDP per capita"

.                 label var lpopl "Population"

.                 label var institution "Institutions"

.                 label var year "Year"

.                 sutex repression year xpers lt loggdp lpopl $conflictvar $leadervar institutio
> ns year, ///
>                         minmax labels file($dir/sumstats.tex) replace
file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data/sumstats.tex saved

.                 
.                 
.                 set seed $seed

.                 xtset gwf_caseid year  /* note that the cross-section unit is the regime-case,
>  not country */
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1955 to 2010
                delta:  1 unit

.                 * check within variation in estimating sample */
.                 local var = "repression xpers"

.                 foreach i of local var {
  2.                         qui xtsum `i'
  3.                         scalar sdb`i' = r(sd_b)
  4.                         scalar sdw`i' = r(sd_w)
  5.                         scalar vart`i'= sdb`i' + sdw`i'
  6.                         scalar varr`i' = sdw`i' / vart`i'
  7.                         scalar list varr`i'
  8.                 }
varrrepression =  .33284995
 varrxpers =  .35873656

.                 
.                 * Missingness not correlated with repression or personalism *
.                 qui:reg repression   xpers lt loggdp lpopl mean5

.                 gen s=e(sample)

.                 gen miss =s==0

.                 tab miss  /* 1.94 percent of sample is missing data on GDP and/or population *
> /

       miss |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      4,144       98.34       98.34
          1 |         70        1.66      100.00
------------+-----------------------------------
      Total |      4,214      100.00

.                 xi:qui xtreg repression i.year miss, cluster(gwf_caseid) fe
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)

.                 lincom miss

 ( 1)  miss = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .2197941   .1760912     1.25   0.213    -.1269523    .5665405
------------------------------------------------------------------------------

.                 xi:qui xtreg xpers i.year miss, cluster(gwf_caseid) fe
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)

.                 lincom miss

 ( 1)  miss = 0

------------------------------------------------------------------------------
       xpers |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0918174   .0724064     1.27   0.206    -.0507603    .2343951
------------------------------------------------------------------------------

.                 drop miss s

.                 
.                 * Fit the data *
.                 twoway scatter repression xpers,mcol(gs13) ||  lfit repression xpers,lcol(blue
> ) || ///
>                         lpolyci repression xpers,col(red) legend(lab(2 "Linear fit") lab(3 "No
> nlinear fit") ///
>                         order(2 3) pos(5) col(1) ring(0))

.                 qui:reg repression xpers  

.                 avplot xpers

.                 qui:reg repression xpers i.year

.                 avplot xpers            

.                 qui:reg repression xpers i.gwf_caseid  

.                 avplot xpers

.                 qui:reg repression xpers i.gwf_caseid i.year

.                 avplot xpers

.                 lvr2plot

.  
.                 * Table 1 FE specifications with case FE and leader cluster *
.                 xi: ivreg2 repression i.gwf_caseid i.year xpers, cluster(gwf_leaderid) partial
> (i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 462               Number of obs =     4214
                                                      F(  1,   461) =    13.89
                                                      Prob > F      =   0.0002
Total (centered) SS     =  817.0320821                Centered R2   =   0.0255
Total (uncentered) SS   =  817.0320821                Uncentered R2 =   0.0255
Residual SS             =  796.2099323                Root MSE      =    .4347

------------------------------------------------------------------------------
             |               Robust
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       xpers |   .5181225   .1335737     3.88   0.000     .2563229     .779922
------------------------------------------------------------------------------
Included instruments: xpers
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_207 _Igwf_casei_208 _Igwf_casei_209
                      _Igwf_casei_210 _Igwf_casei_211 _Igwf_casei_212
                      _Igwf_casei_213 _Igwf_casei_214 _Igwf_casei_215
                      _Igwf_casei_216 _Igwf_casei_217 _Igwf_casei_218
                      _Igwf_casei_219 _Igwf_casei_220 _Igwf_casei_221
                      _Igwf_casei_222 _Igwf_casei_223 _Igwf_casei_224
                      _Igwf_casei_225 _Igwf_casei_226 _Igwf_casei_227
                      _Igwf_casei_228 _Igwf_casei_229 _Igwf_casei_230
                      _Igwf_casei_231 _Igwf_casei_232 _Igwf_casei_233
                      _Igwf_casei_234 _Igwf_casei_235 _Igwf_casei_236
                      _Igwf_casei_237 _Igwf_casei_238 _Igwf_casei_242
                      _Igwf_casei_243 _Igwf_casei_244 _Igwf_casei_245
                      _Igwf_casei_246 _Igwf_casei_247 _Igwf_casei_248
                      _Igwf_casei_250 _Igwf_casei_251 _Igwf_casei_252
                      _Igwf_casei_253 _Igwf_casei_254 _Igwf_casei_255
                      _Igwf_casei_256 _Igwf_casei_258 _Igwf_casei_259
                      _Igwf_casei_260 _Igwf_casei_261 _Igwf_casei_262
                      _Igwf_casei_263 _Igwf_casei_264 _Igwf_casei_265
                      _Igwf_casei_266 _Igwf_casei_267 _Igwf_casei_268
                      _Igwf_casei_269 _Igwf_casei_270 _Igwf_casei_271
                      _Igwf_casei_272 _Igwf_casei_273 _Igwf_casei_274
                      _Igwf_casei_275 _Igwf_casei_276 _Igwf_casei_277
                      _Igwf_casei_278 _Igwf_casei_279 _Igwf_casei_280
                      _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959
                      _Iyear_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963
                      _Iyear_1964 _Iyear_1965 _Iyear_1966 _Iyear_1967
                      _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971
                      _Iyear_1972 _Iyear_1973 _Iyear_1974 _Iyear_1975
                      _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1979
                      _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983
                      _Iyear_1984 _Iyear_1985 _Iyear_1986 _Iyear_1987
                      _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991
                      _Iyear_1992 _Iyear_1993 _Iyear_1994 _Iyear_1995
                      _Iyear_1996 _Iyear_1997 _Iyear_1998 _Iyear_1999
                      _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003
                      _Iyear_2004 _Iyear_2005 _Iyear_2006 _Iyear_2007
                      _Iyear_2008 _Iyear_2009 _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
------------------------------------------------------------------------------

.                 est store r1a

.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl, cluster(gwf_l
> eaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_225

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 458               Number of obs =     4164
                                                      F(  4,   457) =    13.55
                                                      Prob > F      =   0.0000
Total (centered) SS     =  797.0414479                Centered R2   =   0.1021
Total (uncentered) SS   =  797.0414479                Uncentered R2 =   0.1021
Residual SS             =  715.6673531                Root MSE      =    .4146

------------------------------------------------------------------------------
             |               Robust
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       xpers |   .3836489   .1465876     2.62   0.009     .0963425    .6709553
          lt |    .010178   .0236821     0.43   0.667    -.0362381    .0565941
      loggdp |  -.4696158   .0971202    -4.84   0.000    -.6599679   -.2792638
       lpopl |   .5608921   .2994586     1.87   0.061     -.026036     1.14782
------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_207 _Igwf_casei_208 _Igwf_casei_209
                      _Igwf_casei_210 _Igwf_casei_211 _Igwf_casei_212
                      _Igwf_casei_213 _Igwf_casei_214 _Igwf_casei_215
                      _Igwf_casei_216 _Igwf_casei_217 _Igwf_casei_218
                      _Igwf_casei_219 _Igwf_casei_220 _Igwf_casei_221
                      _Igwf_casei_222 _Igwf_casei_223 _Igwf_casei_224
                      _Igwf_casei_226 _Igwf_casei_227 _Igwf_casei_228
                      _Igwf_casei_229 _Igwf_casei_230 _Igwf_casei_231
                      _Igwf_casei_232 _Igwf_casei_233 _Igwf_casei_234
                      _Igwf_casei_235 _Igwf_casei_236 _Igwf_casei_237
                      _Igwf_casei_238 _Igwf_casei_242 _Igwf_casei_243
                      _Igwf_casei_244 _Igwf_casei_245 _Igwf_casei_246
                      _Igwf_casei_247 _Igwf_casei_248 _Igwf_casei_250
                      _Igwf_casei_251 _Igwf_casei_252 _Igwf_casei_253
                      _Igwf_casei_254 _Igwf_casei_255 _Igwf_casei_256
                      _Igwf_casei_258 _Igwf_casei_259 _Igwf_casei_260
                      _Igwf_casei_261 _Igwf_casei_262 _Igwf_casei_263
                      _Igwf_casei_264 _Igwf_casei_265 _Igwf_casei_266
                      _Igwf_casei_267 _Igwf_casei_268 _Igwf_casei_269
                      _Igwf_casei_270 _Igwf_casei_271 _Igwf_casei_272
                      _Igwf_casei_273 _Igwf_casei_274 _Igwf_casei_275
                      _Igwf_casei_276 _Igwf_casei_277 _Igwf_casei_278
                      _Igwf_casei_279 _Igwf_casei_280 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_225
------------------------------------------------------------------------------

.                 est store r1b

.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $conflictvar, 
> cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_225

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 458               Number of obs =     4144
                                                      F(  7,   457) =    16.50
                                                      Prob > F      =   0.0000
Total (centered) SS     =  793.5945414                Centered R2   =   0.1510
Total (uncentered) SS   =  793.5945414                Uncentered R2 =   0.1510
Residual SS             =  673.7700245                Root MSE      =    .4032

------------------------------------------------------------------------------
             |               Robust
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       xpers |   .4468881   .1483794     3.01   0.003     .1560698    .7377064
          lt |   .0178001   .0230834     0.77   0.441    -.0274425    .0630426
      loggdp |  -.4919868   .0981145    -5.01   0.000    -.6842876    -.299686
       lpopl |   .4602975   .2983891     1.54   0.123    -.1245344    1.045129
      civwar |   .3565895   .0689255     5.17   0.000      .221498    .4916809
      intwar |   .3892576   .1021171     3.81   0.000     .1891118    .5894034
       mean5 |   .1610275   .0370414     4.35   0.000     .0884277    .2336273
------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl civwar intwar mean5
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_207 _Igwf_casei_208 _Igwf_casei_209
                      _Igwf_casei_210 _Igwf_casei_211 _Igwf_casei_212
                      _Igwf_casei_213 _Igwf_casei_214 _Igwf_casei_215
                      _Igwf_casei_216 _Igwf_casei_217 _Igwf_casei_218
                      _Igwf_casei_219 _Igwf_casei_220 _Igwf_casei_221
                      _Igwf_casei_222 _Igwf_casei_223 _Igwf_casei_224
                      _Igwf_casei_226 _Igwf_casei_227 _Igwf_casei_228
                      _Igwf_casei_229 _Igwf_casei_230 _Igwf_casei_231
                      _Igwf_casei_232 _Igwf_casei_233 _Igwf_casei_234
                      _Igwf_casei_235 _Igwf_casei_236 _Igwf_casei_237
                      _Igwf_casei_238 _Igwf_casei_242 _Igwf_casei_243
                      _Igwf_casei_244 _Igwf_casei_245 _Igwf_casei_246
                      _Igwf_casei_247 _Igwf_casei_248 _Igwf_casei_250
                      _Igwf_casei_251 _Igwf_casei_252 _Igwf_casei_253
                      _Igwf_casei_254 _Igwf_casei_255 _Igwf_casei_256
                      _Igwf_casei_258 _Igwf_casei_259 _Igwf_casei_260
                      _Igwf_casei_261 _Igwf_casei_262 _Igwf_casei_263
                      _Igwf_casei_264 _Igwf_casei_265 _Igwf_casei_266
                      _Igwf_casei_267 _Igwf_casei_268 _Igwf_casei_269
                      _Igwf_casei_270 _Igwf_casei_271 _Igwf_casei_272
                      _Igwf_casei_273 _Igwf_casei_274 _Igwf_casei_275
                      _Igwf_casei_276 _Igwf_casei_277 _Igwf_casei_278
                      _Igwf_casei_279 _Igwf_casei_280 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_225
------------------------------------------------------------------------------

.                 est store r1c

.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $leadervar, cl
> uster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_225

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 458               Number of obs =     4164
                                                      F(  6,   457) =    10.83
                                                      Prob > F      =   0.0000
Total (centered) SS     =  797.0414479                Centered R2   =   0.1073
Total (uncentered) SS   =  797.0414479                Uncentered R2 =   0.1073
Residual SS             =  711.5062775                Root MSE      =    .4134

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        xpers |   .3699114   .1427235     2.59   0.010     .0901785    .6496444
           lt |   .0159714   .0235546     0.68   0.498    -.0301948    .0621377
       loggdp |  -.4663334   .0962245    -4.85   0.000      -.65493   -.2777368
        lpopl |   .5722405   .2992142     1.91   0.056    -.0142086     1.15869
seniorofficer |   .1494563   .1014721     1.47   0.141    -.0494255     .348338
juniorofficer |  -.1087963   .1621789    -0.67   0.502    -.4266611    .2090684
-------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl seniorofficer juniorofficer
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_207 _Igwf_casei_208 _Igwf_casei_209
                      _Igwf_casei_210 _Igwf_casei_211 _Igwf_casei_212
                      _Igwf_casei_213 _Igwf_casei_214 _Igwf_casei_215
                      _Igwf_casei_216 _Igwf_casei_217 _Igwf_casei_218
                      _Igwf_casei_219 _Igwf_casei_220 _Igwf_casei_221
                      _Igwf_casei_222 _Igwf_casei_223 _Igwf_casei_224
                      _Igwf_casei_226 _Igwf_casei_227 _Igwf_casei_228
                      _Igwf_casei_229 _Igwf_casei_230 _Igwf_casei_231
                      _Igwf_casei_232 _Igwf_casei_233 _Igwf_casei_234
                      _Igwf_casei_235 _Igwf_casei_236 _Igwf_casei_237
                      _Igwf_casei_238 _Igwf_casei_242 _Igwf_casei_243
                      _Igwf_casei_244 _Igwf_casei_245 _Igwf_casei_246
                      _Igwf_casei_247 _Igwf_casei_248 _Igwf_casei_250
                      _Igwf_casei_251 _Igwf_casei_252 _Igwf_casei_253
                      _Igwf_casei_254 _Igwf_casei_255 _Igwf_casei_256
                      _Igwf_casei_258 _Igwf_casei_259 _Igwf_casei_260
                      _Igwf_casei_261 _Igwf_casei_262 _Igwf_casei_263
                      _Igwf_casei_264 _Igwf_casei_265 _Igwf_casei_266
                      _Igwf_casei_267 _Igwf_casei_268 _Igwf_casei_269
                      _Igwf_casei_270 _Igwf_casei_271 _Igwf_casei_272
                      _Igwf_casei_273 _Igwf_casei_274 _Igwf_casei_275
                      _Igwf_casei_276 _Igwf_casei_277 _Igwf_casei_278
                      _Igwf_casei_279 _Igwf_casei_280 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_225
------------------------------------------------------------------------------

.                 est store r1d

.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $conflictvar $
> leadervar, cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_225

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 458               Number of obs =     4144
                                                      F(  9,   457) =    15.44
                                                      Prob > F      =   0.0000
Total (centered) SS     =  793.5945414                Centered R2   =   0.1588
Total (uncentered) SS   =  793.5945414                Uncentered R2 =   0.1588
Residual SS             =  667.6008683                Root MSE      =    .4014

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        xpers |    .426522   .1402157     3.04   0.002     .1517043    .7013397
           lt |   .0258241   .0225948     1.14   0.253    -.0184609    .0701092
       loggdp |  -.4874587   .0964702    -5.05   0.000    -.6765367   -.2983806
        lpopl |   .4759158   .2970235     1.60   0.109    -.1062396    1.058071
       civwar |   .3516045   .0669027     5.26   0.000     .2204777    .4827313
       intwar |   .3972983     .10161     3.91   0.000     .1981464    .5964501
        mean5 |   .1692335   .0360694     4.69   0.000     .0985387    .2399283
seniorofficer |   .2011739   .0967592     2.08   0.038     .0115294    .3908184
juniorofficer |  -.0902097   .1578803    -0.57   0.568    -.3996494      .21923
-------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl civwar intwar mean5 seniorofficer
                      juniorofficer
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_207 _Igwf_casei_208 _Igwf_casei_209
                      _Igwf_casei_210 _Igwf_casei_211 _Igwf_casei_212
                      _Igwf_casei_213 _Igwf_casei_214 _Igwf_casei_215
                      _Igwf_casei_216 _Igwf_casei_217 _Igwf_casei_218
                      _Igwf_casei_219 _Igwf_casei_220 _Igwf_casei_221
                      _Igwf_casei_222 _Igwf_casei_223 _Igwf_casei_224
                      _Igwf_casei_226 _Igwf_casei_227 _Igwf_casei_228
                      _Igwf_casei_229 _Igwf_casei_230 _Igwf_casei_231
                      _Igwf_casei_232 _Igwf_casei_233 _Igwf_casei_234
                      _Igwf_casei_235 _Igwf_casei_236 _Igwf_casei_237
                      _Igwf_casei_238 _Igwf_casei_242 _Igwf_casei_243
                      _Igwf_casei_244 _Igwf_casei_245 _Igwf_casei_246
                      _Igwf_casei_247 _Igwf_casei_248 _Igwf_casei_250
                      _Igwf_casei_251 _Igwf_casei_252 _Igwf_casei_253
                      _Igwf_casei_254 _Igwf_casei_255 _Igwf_casei_256
                      _Igwf_casei_258 _Igwf_casei_259 _Igwf_casei_260
                      _Igwf_casei_261 _Igwf_casei_262 _Igwf_casei_263
                      _Igwf_casei_264 _Igwf_casei_265 _Igwf_casei_266
                      _Igwf_casei_267 _Igwf_casei_268 _Igwf_casei_269
                      _Igwf_casei_270 _Igwf_casei_271 _Igwf_casei_272
                      _Igwf_casei_273 _Igwf_casei_274 _Igwf_casei_275
                      _Igwf_casei_276 _Igwf_casei_277 _Igwf_casei_278
                      _Igwf_casei_279 _Igwf_casei_280 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_225
------------------------------------------------------------------------------

.                 est store r1e

.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $conflictvar $
> leadervar institutions, cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_225

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 458               Number of obs =     4144
                                                      F( 10,   457) =    15.83
                                                      Prob > F      =   0.0000
Total (centered) SS     =  793.5945414                Centered R2   =   0.1657
Total (uncentered) SS   =  793.5945414                Uncentered R2 =   0.1657
Residual SS             =  662.0609704                Root MSE      =    .3997

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        xpers |   .4718774   .1410863     3.34   0.001     .1953533    .7484015
           lt |   .0323857   .0222376     1.46   0.145    -.0111991    .0759705
       loggdp |   -.490839   .0962263    -5.10   0.000     -.679439    -.302239
        lpopl |   .4537159   .2965951     1.53   0.126    -.1275998    1.035032
       civwar |   .3532801   .0655079     5.39   0.000      .224887    .4816732
       intwar |   .3920288   .1068323     3.67   0.000     .1826413    .6014163
        mean5 |    .172273   .0356217     4.84   0.000     .1024557    .2420903
seniorofficer |   .1977576   .0939845     2.10   0.035     .0135514    .3819637
juniorofficer |  -.1009952   .1507042    -0.67   0.503    -.3963701    .1943797
 institutions |  -.1865545   .0713446    -2.61   0.009    -.3263872   -.0467217
-------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl civwar intwar mean5 seniorofficer
                      juniorofficer institutions
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_207 _Igwf_casei_208 _Igwf_casei_209
                      _Igwf_casei_210 _Igwf_casei_211 _Igwf_casei_212
                      _Igwf_casei_213 _Igwf_casei_214 _Igwf_casei_215
                      _Igwf_casei_216 _Igwf_casei_217 _Igwf_casei_218
                      _Igwf_casei_219 _Igwf_casei_220 _Igwf_casei_221
                      _Igwf_casei_222 _Igwf_casei_223 _Igwf_casei_224
                      _Igwf_casei_226 _Igwf_casei_227 _Igwf_casei_228
                      _Igwf_casei_229 _Igwf_casei_230 _Igwf_casei_231
                      _Igwf_casei_232 _Igwf_casei_233 _Igwf_casei_234
                      _Igwf_casei_235 _Igwf_casei_236 _Igwf_casei_237
                      _Igwf_casei_238 _Igwf_casei_242 _Igwf_casei_243
                      _Igwf_casei_244 _Igwf_casei_245 _Igwf_casei_246
                      _Igwf_casei_247 _Igwf_casei_248 _Igwf_casei_250
                      _Igwf_casei_251 _Igwf_casei_252 _Igwf_casei_253
                      _Igwf_casei_254 _Igwf_casei_255 _Igwf_casei_256
                      _Igwf_casei_258 _Igwf_casei_259 _Igwf_casei_260
                      _Igwf_casei_261 _Igwf_casei_262 _Igwf_casei_263
                      _Igwf_casei_264 _Igwf_casei_265 _Igwf_casei_266
                      _Igwf_casei_267 _Igwf_casei_268 _Igwf_casei_269
                      _Igwf_casei_270 _Igwf_casei_271 _Igwf_casei_272
                      _Igwf_casei_273 _Igwf_casei_274 _Igwf_casei_275
                      _Igwf_casei_276 _Igwf_casei_277 _Igwf_casei_278
                      _Igwf_casei_279 _Igwf_casei_280 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_225
------------------------------------------------------------------------------

.                 est store r1f

.                 label var repress "Repression"

.                 label var xpers "{bf:Personalism}"

.                 label var senior "Senior officer"

.                 label var junior "Junior officer"

.                 label var civwar "Civil conflict"

.                 label var intwar "Int'l conflict"

.                 label var mean5 "Protest"

.                 label var ld "Regime duration"

.                 label var lt "Leader time in power"

.                 label var loggdp "GDP per capita"

.                 label var lpopl "Population"

.                 label var institution "Institutions"

.                 coefplot (r1a, msym(d)) (r1b, msym(t)) (r1c, msym(oh)) (r1d, msym(plus)) (r1e,
>  msym(P)) (r1f, msym(T)), ///
>                 title("Correlates of domestic repression", size(medium)) drop(_cons  _I*) orde
> r(xpers) xline(0) ///
>                 grid(glcolor(gs15)) mfcolor(white) xlabel(-.5 (.5) 1)  levels(95 90)  ///
>                 legend(off) ysize(2) xsize(1.5)  xtitle("Coefficient estimate", height(6)) ///
>                 note("90 (thin) and 95 (thick) percent confidence intervals", size(vsmall) pos
> (6))
(note:  named style P not found in class symbol, default attributes used)

.                 graph export "$dir\repression-model-1.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\repression-model-1.p
> df written in PDF format)

.                 estout r1a r1b r1c r1d r1e r1f using TableA1.tex, cells(b(star  fmt(%9.3f)) se
> (par fmt(%9.2f))) ///
>                 stats(r2 N N_clust) style(tex) replace label starlevels(* 0.05) title(\label{t
> abA1})            
(output written to TableA1.tex)

.                                         
.                 * show fe/re result with regime-case cluster *
.                 xi: qui xtreg repression i.year xpers lt loggdp lpopl $conflictvar $leadervar 
> institutions, fe cluster(gwf_caseid)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)

.                 lincom xpers

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .4718774   .1576824     2.99   0.003     .1613747    .7823801
------------------------------------------------------------------------------

.                 qui xtreg repression coldwar xpers lt loggdp lpopl $conflictvar $leadervar ins
> titutions, fe cluster(gwf_caseid)

.                 lincom xpers  /* year FE and coldwar give roughly same estimate on xpers */

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .5287804   .1585865     3.33   0.001     .2164974    .8410635
------------------------------------------------------------------------------

.                         * Hausman test *
.                                 qui xtreg repression coldwar xpers lt loggdp lpopl $conflictva
> r $leadervar institutions, fe  

.                                 est store fixed

.                                 qui xtreg repression coldwar xpers lt loggdp lpopl $conflictva
> r $leadervar institutions, re  

.                                 est store random

.                                 hausman fixed ., sigmamore constant  /* rejects RE model in fa
> vor of FE model; personalism estimate statistically the same in both */

                 ---- Coefficients ----
             |      (b)          (B)            (b-B)     sqrt(diag(V_b-V_B))
             |     fixed        random       Difference          S.E.
-------------+----------------------------------------------------------------
     coldwar |    .3412781     .3358402        .0054379        .0138357
       xpers |    .5287804     .5405523       -.0117719        .0155984
          lt |    .0349809     .0294665        .0055144        .0022283
      loggdp |    -.419217    -.3872768       -.0319402        .0123742
       lpopl |    .3126569     .3086372        .0040197        .0399813
      civwar |    .3910747     .4345056       -.0434309        .0080871
      intwar |    .4390516     .4790898       -.0400382        .0097965
       mean5 |    .1649967     .1639027         .001094        .0038138
senioroffi~r |    .1590184     .1568157        .0022027        .0173367
junioroffi~r |   -.1224586    -.1239205        .0014619        .0364955
institutions |   -.2511974    -.2523718        .0011744        .0080467
       _cons |   -.3583214      -.35017       -.0081514               .
------------------------------------------------------------------------------
                           b = consistent under Ho and Ha; obtained from xtreg
            B = inconsistent under Ha, efficient under Ho; obtained from xtreg

    Test:  Ho:  difference in coefficients not systematic

                 chi2(12) = (b-B)'[(V_b-V_B)^(-1)](b-B)
                          =       63.44
                Prob>chi2 =      0.0000
                (V_b-V_B is not positive definite)

.                 qui xtreg repression coldwar xpers lt loggdp lpopl $conflictvar $leadervar ins
> titutions, re cluster(gwf_caseid) theta

.                 lincom xpers

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .5405523   .1474561     3.67   0.000     .2515437    .8295609
------------------------------------------------------------------------------

.                 scalar er       =e(sigma_e)

.                 scalar ur_re    =e(sigma_u)

.                 scalar theta_med=e(thta_50)

.                 egen count =count(year) if e(sample),by(gwf_caseid)
(70 missing values generated)

.                 egen tag=tag(gwf_caseid)

.                 hist count if tag==1,bin(20)
(bin=20, start=1, width=2.75)

.                 gen theta_re=1-sqrt((er^2)/(count*ur_re^2+er^2))
(70 missing values generated)

.                 hist theta if tag==1,bin(50) xlab(.4(.1).9) freq xtitle(Theta)  /* thetas are 
> large; unit means weighted close to one for many panels */
(bin=50, start=.47393608, width=.00887369)

.                 graph export "$dir\thetas.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\thetas.pdf written i
> n PDF format)

.                 drop theta count tag

.                 
.                 * RE specifications *
.                 global leadervar="seniorofficer juniorofficer"

.                 global conflictvar = "civwar intwar mean5"

.                 xtreg repression coldwar xpers, cluster(gwf_caseid)

Random-effects GLS regression                   Number of obs     =      4,214
Group variable: gwf_caseid                      Number of groups  =        261

R-sq:                                           Obs per group:
     within  = 0.0772                                         min =          1
     between = 0.0027                                         avg =       16.1
     overall = 0.0228                                         max =         56

                                                Wald chi2(2)      =      20.70
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                           (Std. Err. adjusted for 261 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     coldwar |   .3263497   .0956864     3.41   0.001     .1388078    .5138916
       xpers |   .5041041   .1411098     3.57   0.000      .227534    .7806742
       _cons |  -.2970767   .1062843    -2.80   0.005    -.5053901   -.0887632
-------------+----------------------------------------------------------------
     sigma_u |  .90688941
     sigma_e |  .45998611
         rho |  .79537724   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                 est store r2a

.                 xtreg repression coldwar xpers lt loggdp lpopl, cluster(gwf_caseid)

Random-effects GLS regression                   Number of obs     =      4,164
Group variable: gwf_caseid                      Number of groups  =        260

R-sq:                                           Obs per group:
     within  = 0.1241                                         min =          1
     between = 0.2047                                         avg =       16.0
     overall = 0.2745                                         max =         56

                                                Wald chi2(5)      =      69.80
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                           (Std. Err. adjusted for 260 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     coldwar |   .3524736   .0832046     4.24   0.000     .1893955    .5155516
       xpers |   .4335246   .1531991     2.83   0.005     .1332598    .7337894
          lt |   .0027519   .0244366     0.11   0.910     -.045143    .0506468
      loggdp |  -.3737427   .0813144    -4.60   0.000    -.5331161   -.2143694
       lpopl |   .4346109   .0884624     4.91   0.000     .2612277     .607994
       _cons |  -.3299719   .0993679    -3.32   0.001    -.5247293   -.1352145
-------------+----------------------------------------------------------------
     sigma_u |  .77418971
     sigma_e |  .44259382
         rho |  .75367862   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                 est store r2b

.                 xtreg repression coldwar xpers lt loggdp lpopl $conflictvar, cluster(gwf_casei
> d)

Random-effects GLS regression                   Number of obs     =      4,144
Group variable: gwf_caseid                      Number of groups  =        260

R-sq:                                           Obs per group:
     within  = 0.1748                                         min =          1
     between = 0.2827                                         avg =       15.9
     overall = 0.3410                                         max =         56

                                                Wald chi2(8)      =     154.56
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                           (Std. Err. adjusted for 260 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     coldwar |   .3636933   .0814235     4.47   0.000     .2041061    .5232805
       xpers |   .4883818   .1536437     3.18   0.001     .1872456    .7895179
          lt |   .0127628    .023206     0.55   0.582    -.0327202    .0582457
      loggdp |  -.3873715   .0791216    -4.90   0.000    -.5424469   -.2322961
       lpopl |   .3288111   .0782695     4.20   0.000     .1754056    .4822166
      civwar |   .4402612   .0753018     5.85   0.000     .2926724    .5878499
      intwar |   .4844281    .107684     4.50   0.000     .2733713    .6954849
       mean5 |   .1550909   .0425425     3.65   0.000     .0717091    .2384726
       _cons |  -.4279156   .1009203    -4.24   0.000    -.6257157   -.2301155
-------------+----------------------------------------------------------------
     sigma_u |  .68972513
     sigma_e |  .43038671
         rho |  .71974923   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                 est store r2c

.                 xtreg repression coldwar xpers lt loggdp lpopl $leadervar, cluster(gwf_caseid)

Random-effects GLS regression                   Number of obs     =      4,164
Group variable: gwf_caseid                      Number of groups  =        260

R-sq:                                           Obs per group:
     within  = 0.1274                                         min =          1
     between = 0.2159                                         avg =       16.0
     overall = 0.2793                                         max =         56

                                                Wald chi2(7)      =      81.07
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                            (Std. Err. adjusted for 260 clusters in gwf_caseid)
-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
      coldwar |   .3523663    .082702     4.26   0.000     .1902733    .5144593
        xpers |    .427894   .1488642     2.87   0.004     .1361256    .7196625
           lt |    .007195    .023812     0.30   0.763    -.0394756    .0538657
       loggdp |  -.3796229   .0812267    -4.67   0.000    -.5388242   -.2204216
        lpopl |   .4097511   .0901348     4.55   0.000     .2330902     .586412
seniorofficer |   .1263842   .1333399     0.95   0.343    -.1349573    .3877257
juniorofficer |  -.1109253   .1170108    -0.95   0.343    -.3402623    .1184117
        _cons |  -.3407068   .0944699    -3.61   0.000    -.5258645   -.1555491
--------------+----------------------------------------------------------------
      sigma_u |  .77121888
      sigma_e |  .44185417
          rho |  .75287123   (fraction of variance due to u_i)
-------------------------------------------------------------------------------

.                 est store r2d

.                 xtreg repression coldwar xpers lt loggdp lpopl $conflictvar $leadervar, cluste
> r(gwf_caseid)

Random-effects GLS regression                   Number of obs     =      4,144
Group variable: gwf_caseid                      Number of groups  =        260

R-sq:                                           Obs per group:
     within  = 0.1801                                         min =          1
     between = 0.2922                                         avg =       15.9
     overall = 0.3446                                         max =         56

                                                Wald chi2(10)     =     171.61
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                            (Std. Err. adjusted for 260 clusters in gwf_caseid)
-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
      coldwar |   .3645524   .0805469     4.53   0.000     .2066834    .5224215
        xpers |   .4789059   .1449088     3.30   0.001     .1948899    .7629219
           lt |   .0187742   .0221642     0.85   0.397    -.0246668    .0622152
       loggdp |  -.3954192    .078767    -5.02   0.000    -.5497996   -.2410388
        lpopl |   .2964415   .0804585     3.68   0.000     .1387457    .4541374
       civwar |   .4367712   .0721658     6.05   0.000     .2953289    .5782136
       intwar |   .4916059   .1068109     4.60   0.000     .2822604    .7009514
        mean5 |   .1595217   .0414467     3.85   0.000     .0782877    .2407556
seniorofficer |    .164875   .1197537     1.38   0.169     -.069838     .399588
juniorofficer |  -.0994759   .1069847    -0.93   0.352    -.3091621    .1102104
        _cons |  -.4519822   .0927126    -4.88   0.000    -.6336955   -.2702688
--------------+----------------------------------------------------------------
      sigma_u |  .68737125
      sigma_e |  .42912202
          rho |  .71955724   (fraction of variance due to u_i)
-------------------------------------------------------------------------------

.                 est store r2e

.                 xtreg repression coldwar xpers lt loggdp lpopl $conflictvar $leadervar institu
> tions, cluster(gwf_caseid)

Random-effects GLS regression                   Number of obs     =      4,144
Group variable: gwf_caseid                      Number of groups  =        260

R-sq:                                           Obs per group:
     within  = 0.1922                                         min =          1
     between = 0.3016                                         avg =       15.9
     overall = 0.3532                                         max =         56

                                                Wald chi2(11)     =     208.06
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                            (Std. Err. adjusted for 260 clusters in gwf_caseid)
-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
      coldwar |   .3358402   .0804093     4.18   0.000     .1782409    .4934395
        xpers |   .5405523   .1474561     3.67   0.000     .2515437    .8295609
           lt |   .0294665   .0219603     1.34   0.180    -.0135749    .0725079
       loggdp |  -.3872768   .0793098    -4.88   0.000    -.5427212   -.2318324
        lpopl |   .3086372   .0800089     3.86   0.000     .1518227    .4654517
       civwar |   .4345056   .0693673     6.26   0.000     .2985483    .5704629
       intwar |   .4790898   .1194836     4.01   0.000     .2449062    .7132734
        mean5 |   .1639027   .0408795     4.01   0.000     .0837803    .2440251
seniorofficer |   .1568157    .112699     1.39   0.164    -.0640702    .3777016
juniorofficer |  -.1239205   .0991287    -1.25   0.211    -.3182092    .0703682
 institutions |  -.2523718   .0796301    -3.17   0.002     -.408444   -.0962996
        _cons |    -.35017   .1005187    -3.48   0.000    -.5471831    -.153157
--------------+----------------------------------------------------------------
      sigma_u |  .68873068
      sigma_e |  .42603149
          rho |  .72325675   (fraction of variance due to u_i)
-------------------------------------------------------------------------------

.                 est store r2f

.                 label var repress "Repression"

.                 label var xpers "{bf:Personalism}"

.                 label var senior "Senior officer"

.                 label var junior "Junior officer"

.                 label var civwar "Civil conflict"

.                 label var intwar "Int'l conflict"

.                 label var mean5 "Protest"

.                 label var ld "Regime duration"

.                 label var lt "Leader time in power"

.                 label var loggdp "GDP per capita"

.                 label var lpopl "Population"

.                 label var institution "Institutions"

.                 label var coldwar `""Cold war"  "(1955-1989)""'

.                 coefplot (r2a, msym(d)) (r2b, msym(t)) (r2c, msym(oh)) (r2d, msym(plus)) (r2e,
>  msym(P)) (r2f, msym(T)), ///
>                 title("Regime-case RE models", size(medium)) drop(_cons  _I*) order(xpers) xli
> ne(0) ///
>                 grid(glcolor(gs15)) mfcolor(white) xlabel(-.5 (.5) 1)  levels(95 90)  ///
>                 legend(off) ysize(2) xsize(1.5) saving(r1, replace)     xtitle("Coefficient es
> timate", height(6)) ///
>                 note("90 (thin) and 95 (thick) percent confidence intervals", size(vsmall) pos
> (6))
(note:  named style P not found in class symbol, default attributes used)
(file r1.gph saved)

.                 graph export "$dir\repression-model-2.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\repression-model-2.p
> df written in PDF format)

.                 
.                 * check with HAC errors *
.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $conflictvar $
> leadervar institution, ///
>                         rob bw(7) partial(i.gwf_caseid i.year)  
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_225

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and autocorrelation
  kernel=Bartlett; bandwidth=     7
  time variable (t):  year
  group variable (i): gwf_caseid

                                                      Number of obs =     4144
                                                      F( 10,  3819) =    19.23
                                                      Prob > F      =   0.0000
Total (centered) SS     =  793.5945414                Centered R2   =   0.1657
Total (uncentered) SS   =  793.5945414                Uncentered R2 =   0.1657
Residual SS             =  662.0609704                Root MSE      =    .3997

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        xpers |   .4718774   .1109073     4.25   0.000     .2545031    .6892517
           lt |   .0323857   .0176435     1.84   0.066    -.0021949    .0669662
       loggdp |   -.490839   .0751377    -6.53   0.000    -.6381061   -.3435719
        lpopl |   .4537159   .2208984     2.05   0.040     .0207631    .8866688
       civwar |   .3532801   .0641029     5.51   0.000     .2276406    .4789196
       intwar |   .3920288   .0887636     4.42   0.000     .2180553    .5660023
        mean5 |    .172273   .0329857     5.22   0.000     .1076223    .2369238
seniorofficer |   .1977576   .0840164     2.35   0.019     .0330885    .3624267
juniorofficer |  -.1009952   .1345985    -0.75   0.453    -.3648035     .162813
 institutions |  -.1865545   .0589883    -3.16   0.002    -.3021695   -.0709395
-------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl civwar intwar mean5 seniorofficer
                      juniorofficer institutions
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_207 _Igwf_casei_208 _Igwf_casei_209
                      _Igwf_casei_210 _Igwf_casei_211 _Igwf_casei_212
                      _Igwf_casei_213 _Igwf_casei_214 _Igwf_casei_215
                      _Igwf_casei_216 _Igwf_casei_217 _Igwf_casei_218
                      _Igwf_casei_219 _Igwf_casei_220 _Igwf_casei_221
                      _Igwf_casei_222 _Igwf_casei_223 _Igwf_casei_224
                      _Igwf_casei_226 _Igwf_casei_227 _Igwf_casei_228
                      _Igwf_casei_229 _Igwf_casei_230 _Igwf_casei_231
                      _Igwf_casei_232 _Igwf_casei_233 _Igwf_casei_234
                      _Igwf_casei_235 _Igwf_casei_236 _Igwf_casei_237
                      _Igwf_casei_238 _Igwf_casei_242 _Igwf_casei_243
                      _Igwf_casei_244 _Igwf_casei_245 _Igwf_casei_246
                      _Igwf_casei_247 _Igwf_casei_248 _Igwf_casei_250
                      _Igwf_casei_251 _Igwf_casei_252 _Igwf_casei_253
                      _Igwf_casei_254 _Igwf_casei_255 _Igwf_casei_256
                      _Igwf_casei_258 _Igwf_casei_259 _Igwf_casei_260
                      _Igwf_casei_261 _Igwf_casei_262 _Igwf_casei_263
                      _Igwf_casei_264 _Igwf_casei_265 _Igwf_casei_266
                      _Igwf_casei_267 _Igwf_casei_268 _Igwf_casei_269
                      _Igwf_casei_270 _Igwf_casei_271 _Igwf_casei_272
                      _Igwf_casei_273 _Igwf_casei_274 _Igwf_casei_275
                      _Igwf_casei_276 _Igwf_casei_277 _Igwf_casei_278
                      _Igwf_casei_279 _Igwf_casei_280 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_225
------------------------------------------------------------------------------

.                 * add leader age to specification *
.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $conflictvar $
> leadervar institution G_age, ///
>                         cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_207 _Igwf_casei_225

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 450               Number of obs =     4132
                                                      F( 11,   449) =    16.21
                                                      Prob > F      =   0.0000
Total (centered) SS     =  792.2768042                Centered R2   =   0.1855
Total (uncentered) SS   =  792.2768042                Uncentered R2 =   0.1855
Residual SS             =   645.296355                Root MSE      =    .3952

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        xpers |   .4739575   .1380699     3.43   0.001     .2033455    .7445695
           lt |   .0876018   .0242257     3.62   0.000     .0401202    .1350834
       loggdp |  -.4919475   .0966415    -5.09   0.000    -.6813613   -.3025336
        lpopl |   .4381104   .2784229     1.57   0.116    -.1075884    .9838093
       civwar |   .3523047   .0652347     5.40   0.000      .224447    .4801623
       intwar |   .4358203   .1187228     3.67   0.000     .2031278    .6685127
        mean5 |   .1828231   .0359951     5.08   0.000      .112274    .2533723
seniorofficer |    .207368   .0877165     2.36   0.018     .0354469    .3792892
juniorofficer |   -.127648   .1446111    -0.88   0.377    -.4110806    .1557846
 institutions |  -.1638222   .0726152    -2.26   0.024    -.3061453   -.0214991
        G_age |  -.1315741   .0371025    -3.55   0.000    -.2042936   -.0588546
-------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl civwar intwar mean5 seniorofficer
                      juniorofficer institutions G_age
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_208 _Igwf_casei_209 _Igwf_casei_210
                      _Igwf_casei_211 _Igwf_casei_212 _Igwf_casei_213
                      _Igwf_casei_214 _Igwf_casei_215 _Igwf_casei_216
                      _Igwf_casei_217 _Igwf_casei_218 _Igwf_casei_219
                      _Igwf_casei_220 _Igwf_casei_221 _Igwf_casei_222
                      _Igwf_casei_223 _Igwf_casei_224 _Igwf_casei_226
                      _Igwf_casei_227 _Igwf_casei_228 _Igwf_casei_229
                      _Igwf_casei_230 _Igwf_casei_231 _Igwf_casei_232
                      _Igwf_casei_233 _Igwf_casei_234 _Igwf_casei_235
                      _Igwf_casei_236 _Igwf_casei_237 _Igwf_casei_238
                      _Igwf_casei_242 _Igwf_casei_243 _Igwf_casei_244
                      _Igwf_casei_245 _Igwf_casei_246 _Igwf_casei_247
                      _Igwf_casei_248 _Igwf_casei_250 _Igwf_casei_251
                      _Igwf_casei_252 _Igwf_casei_253 _Igwf_casei_254
                      _Igwf_casei_255 _Igwf_casei_256 _Igwf_casei_258
                      _Igwf_casei_259 _Igwf_casei_260 _Igwf_casei_261
                      _Igwf_casei_262 _Igwf_casei_263 _Igwf_casei_264
                      _Igwf_casei_265 _Igwf_casei_266 _Igwf_casei_267
                      _Igwf_casei_268 _Igwf_casei_269 _Igwf_casei_270
                      _Igwf_casei_271 _Igwf_casei_272 _Igwf_casei_273
                      _Igwf_casei_274 _Igwf_casei_275 _Igwf_casei_276
                      _Igwf_casei_277 _Igwf_casei_278 _Igwf_casei_279
                      _Igwf_casei_280 _Iyear_1956 _Iyear_1957 _Iyear_1958
                      _Iyear_1959 _Iyear_1960 _Iyear_1961 _Iyear_1962
                      _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1966
                      _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970
                      _Iyear_1971 _Iyear_1972 _Iyear_1973 _Iyear_1974
                      _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978
                      _Iyear_1979 _Iyear_1980 _Iyear_1981 _Iyear_1982
                      _Iyear_1983 _Iyear_1984 _Iyear_1985 _Iyear_1986
                      _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990
                      _Iyear_1991 _Iyear_1992 _Iyear_1993 _Iyear_1994
                      _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998
                      _Iyear_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002
                      _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                      _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_207 _Igwf_casei_225
------------------------------------------------------------------------------

.                         est store t1

.                 * add NAVCO protests to specification, only up to 2006 *
.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $conflictvar $
> leadervar institution nav_protestV nav_protestNV, ///
>                         cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_20 _Igwf_casei_114 _Igwf_casei_117
                    _Igwf_casei_152 _Igwf_casei_162 _Igwf_casei_171
                    _Igwf_casei_207 _Igwf_casei_221 _Igwf_casei_224
                    _Igwf_casei_225 _Igwf_casei_242 _Igwf_casei_243
                    _Igwf_casei_253 _Igwf_casei_280 _Iyear_2008 _Iyear_2009
                    _Iyear_2010

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 439               Number of obs =     3899
                                                      F( 12,   438) =    20.38
                                                      Prob > F      =   0.0000
Total (centered) SS     =  726.9552554                Centered R2   =   0.2197
Total (uncentered) SS   =  726.9552554                Uncentered R2 =   0.2197
Residual SS             =  567.2266381                Root MSE      =    .3814

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        xpers |   .4426487   .1329731     3.33   0.001     .1820261    .7032713
           lt |   .0331253   .0221688     1.49   0.135    -.0103248    .0765753
       loggdp |  -.4646646   .0955451    -4.86   0.000    -.6519295   -.2773997
        lpopl |   .6646044   .3096678     2.15   0.032     .0576666    1.271542
       civwar |   .2351216   .0535247     4.39   0.000      .130215    .3400281
       intwar |   .2528099   .0979052     2.58   0.010     .0609193    .4447005
        mean5 |   .1426218   .0352418     4.05   0.000     .0735492    .2116944
seniorofficer |   .1806983   .1045428     1.73   0.084    -.0242018    .3855984
juniorofficer |  -.1138304   .1554093    -0.73   0.464     -.418427    .1907662
 institutions |  -.1464015   .0694163    -2.11   0.035    -.2824549    -.010348
 nav_protestV |   .4523626   .0546281     8.28   0.000     .3452934    .5594318
nav_protestNV |   .0524247   .0680793     0.77   0.441    -.0810083    .1858577
-------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl civwar intwar mean5 seniorofficer
                      juniorofficer institutions nav_protestV nav_protestNV
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_21 _Igwf_casei_22 _Igwf_casei_23
                      _Igwf_casei_24 _Igwf_casei_25 _Igwf_casei_26
                      _Igwf_casei_27 _Igwf_casei_28 _Igwf_casei_29
                      _Igwf_casei_30 _Igwf_casei_31 _Igwf_casei_35
                      _Igwf_casei_36 _Igwf_casei_37 _Igwf_casei_38
                      _Igwf_casei_39 _Igwf_casei_40 _Igwf_casei_41
                      _Igwf_casei_42 _Igwf_casei_43 _Igwf_casei_44
                      _Igwf_casei_45 _Igwf_casei_46 _Igwf_casei_47
                      _Igwf_casei_48 _Igwf_casei_49 _Igwf_casei_50
                      _Igwf_casei_51 _Igwf_casei_52 _Igwf_casei_53
                      _Igwf_casei_54 _Igwf_casei_55 _Igwf_casei_56
                      _Igwf_casei_57 _Igwf_casei_58 _Igwf_casei_59
                      _Igwf_casei_60 _Igwf_casei_61 _Igwf_casei_62
                      _Igwf_casei_63 _Igwf_casei_64 _Igwf_casei_65
                      _Igwf_casei_66 _Igwf_casei_67 _Igwf_casei_68
                      _Igwf_casei_70 _Igwf_casei_71 _Igwf_casei_72
                      _Igwf_casei_73 _Igwf_casei_74 _Igwf_casei_75
                      _Igwf_casei_76 _Igwf_casei_78 _Igwf_casei_79
                      _Igwf_casei_80 _Igwf_casei_81 _Igwf_casei_82
                      _Igwf_casei_83 _Igwf_casei_85 _Igwf_casei_86
                      _Igwf_casei_87 _Igwf_casei_89 _Igwf_casei_91
                      _Igwf_casei_92 _Igwf_casei_93 _Igwf_casei_94
                      _Igwf_casei_95 _Igwf_casei_96 _Igwf_casei_97
                      _Igwf_casei_98 _Igwf_casei_99 _Igwf_casei_100
                      _Igwf_casei_101 _Igwf_casei_102 _Igwf_casei_103
                      _Igwf_casei_104 _Igwf_casei_105 _Igwf_casei_106
                      _Igwf_casei_107 _Igwf_casei_108 _Igwf_casei_109
                      _Igwf_casei_110 _Igwf_casei_111 _Igwf_casei_112
                      _Igwf_casei_113 _Igwf_casei_115 _Igwf_casei_116
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_153 _Igwf_casei_154 _Igwf_casei_155
                      _Igwf_casei_156 _Igwf_casei_157 _Igwf_casei_158
                      _Igwf_casei_159 _Igwf_casei_160 _Igwf_casei_161
                      _Igwf_casei_163 _Igwf_casei_164 _Igwf_casei_165
                      _Igwf_casei_166 _Igwf_casei_167 _Igwf_casei_168
                      _Igwf_casei_169 _Igwf_casei_170 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_208 _Igwf_casei_209 _Igwf_casei_210
                      _Igwf_casei_211 _Igwf_casei_212 _Igwf_casei_213
                      _Igwf_casei_214 _Igwf_casei_215 _Igwf_casei_216
                      _Igwf_casei_217 _Igwf_casei_218 _Igwf_casei_219
                      _Igwf_casei_220 _Igwf_casei_222 _Igwf_casei_223
                      _Igwf_casei_226 _Igwf_casei_227 _Igwf_casei_228
                      _Igwf_casei_229 _Igwf_casei_230 _Igwf_casei_231
                      _Igwf_casei_232 _Igwf_casei_233 _Igwf_casei_234
                      _Igwf_casei_235 _Igwf_casei_236 _Igwf_casei_237
                      _Igwf_casei_238 _Igwf_casei_244 _Igwf_casei_245
                      _Igwf_casei_246 _Igwf_casei_247 _Igwf_casei_248
                      _Igwf_casei_250 _Igwf_casei_251 _Igwf_casei_252
                      _Igwf_casei_254 _Igwf_casei_255 _Igwf_casei_256
                      _Igwf_casei_258 _Igwf_casei_259 _Igwf_casei_260
                      _Igwf_casei_261 _Igwf_casei_262 _Igwf_casei_263
                      _Igwf_casei_264 _Igwf_casei_265 _Igwf_casei_266
                      _Igwf_casei_267 _Igwf_casei_268 _Igwf_casei_269
                      _Igwf_casei_270 _Igwf_casei_271 _Igwf_casei_272
                      _Igwf_casei_273 _Igwf_casei_274 _Igwf_casei_275
                      _Igwf_casei_276 _Igwf_casei_277 _Igwf_casei_278
                      _Igwf_casei_279 _Iyear_1956 _Iyear_1957 _Iyear_1958
                      _Iyear_1959 _Iyear_1960 _Iyear_1961 _Iyear_1962
                      _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1966
                      _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970
                      _Iyear_1971 _Iyear_1972 _Iyear_1973 _Iyear_1974
                      _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978
                      _Iyear_1979 _Iyear_1980 _Iyear_1981 _Iyear_1982
                      _Iyear_1983 _Iyear_1984 _Iyear_1985 _Iyear_1986
                      _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990
                      _Iyear_1991 _Iyear_1992 _Iyear_1993 _Iyear_1994
                      _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998
                      _Iyear_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002
                      _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                      _Iyear_2007 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_20 _Igwf_casei_114 _Igwf_casei_117
                      _Igwf_casei_152 _Igwf_casei_162 _Igwf_casei_171
                      _Igwf_casei_207 _Igwf_casei_221 _Igwf_casei_224
                      _Igwf_casei_225 _Igwf_casei_242 _Igwf_casei_243
                      _Igwf_casei_253 _Igwf_casei_280 _Iyear_2008 _Iyear_2009
                      _Iyear_2010
------------------------------------------------------------------------------

.                         est store t2

.                 * add judicial independence to specification *
.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $conflictvar $
> leadervar institution  lji, ///
>                         cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_225 _Igwf_casei_228 _Igwf_casei_229

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 453               Number of obs =     4120
                                                      F( 11,   452) =    16.74
                                                      Prob > F      =   0.0000
Total (centered) SS     =  792.4139211                Centered R2   =   0.1784
Total (uncentered) SS   =  792.4139211                Uncentered R2 =   0.1784
Residual SS             =  651.0303332                Root MSE      =    .3975

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        xpers |   .4407529    .138893     3.17   0.002     .1685276    .7129782
           lt |   .0287305   .0225392     1.27   0.202    -.0154456    .0729067
       loggdp |  -.4656758   .0987876    -4.71   0.000     -.659296   -.2720556
        lpopl |   .5439728   .2978627     1.83   0.068    -.0398274    1.127773
       civwar |   .3326913   .0666185     4.99   0.000     .2021214    .4632611
       intwar |   .4124397   .1037009     3.98   0.000     .2091896    .6156897
        mean5 |   .1791422   .0359483     4.98   0.000     .1086849    .2495995
seniorofficer |   .1884067   .0961388     1.96   0.050    -.0000219    .3768354
juniorofficer |  -.1053818   .1504927    -0.70   0.484    -.4003422    .1895785
 institutions |  -.1582452   .0713409    -2.22   0.027    -.2980708   -.0184196
          lji |  -.8701194   .3146153    -2.77   0.006    -1.486754   -.2534848
-------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl civwar intwar mean5 seniorofficer
                      juniorofficer institutions lji
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_207 _Igwf_casei_208 _Igwf_casei_209
                      _Igwf_casei_210 _Igwf_casei_211 _Igwf_casei_212
                      _Igwf_casei_213 _Igwf_casei_214 _Igwf_casei_215
                      _Igwf_casei_216 _Igwf_casei_217 _Igwf_casei_218
                      _Igwf_casei_219 _Igwf_casei_220 _Igwf_casei_221
                      _Igwf_casei_222 _Igwf_casei_223 _Igwf_casei_224
                      _Igwf_casei_226 _Igwf_casei_227 _Igwf_casei_230
                      _Igwf_casei_231 _Igwf_casei_232 _Igwf_casei_233
                      _Igwf_casei_234 _Igwf_casei_235 _Igwf_casei_236
                      _Igwf_casei_237 _Igwf_casei_238 _Igwf_casei_242
                      _Igwf_casei_243 _Igwf_casei_244 _Igwf_casei_245
                      _Igwf_casei_246 _Igwf_casei_247 _Igwf_casei_248
                      _Igwf_casei_250 _Igwf_casei_251 _Igwf_casei_252
                      _Igwf_casei_253 _Igwf_casei_254 _Igwf_casei_255
                      _Igwf_casei_256 _Igwf_casei_258 _Igwf_casei_259
                      _Igwf_casei_260 _Igwf_casei_261 _Igwf_casei_262
                      _Igwf_casei_263 _Igwf_casei_264 _Igwf_casei_265
                      _Igwf_casei_266 _Igwf_casei_267 _Igwf_casei_268
                      _Igwf_casei_269 _Igwf_casei_270 _Igwf_casei_271
                      _Igwf_casei_272 _Igwf_casei_273 _Igwf_casei_274
                      _Igwf_casei_275 _Igwf_casei_276 _Igwf_casei_277
                      _Igwf_casei_278 _Igwf_casei_279 _Igwf_casei_280
                      _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959
                      _Iyear_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963
                      _Iyear_1964 _Iyear_1965 _Iyear_1966 _Iyear_1967
                      _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971
                      _Iyear_1972 _Iyear_1973 _Iyear_1974 _Iyear_1975
                      _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1979
                      _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983
                      _Iyear_1984 _Iyear_1985 _Iyear_1986 _Iyear_1987
                      _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991
                      _Iyear_1992 _Iyear_1993 _Iyear_1994 _Iyear_1995
                      _Iyear_1996 _Iyear_1997 _Iyear_1998 _Iyear_1999
                      _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003
                      _Iyear_2004 _Iyear_2005 _Iyear_2006 _Iyear_2007
                      _Iyear_2008 _Iyear_2009 _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_225 _Igwf_casei_228 _Igwf_casei_229
------------------------------------------------------------------------------

.                         est store t3

.                 * add elections to specification *
.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $conflictvar $
> leadervar institution mpartyelec, ///
>                         cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_225

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 458               Number of obs =     4144
                                                      F( 11,   457) =    14.56
                                                      Prob > F      =   0.0000
Total (centered) SS     =  793.5945414                Centered R2   =   0.1660
Total (uncentered) SS   =  793.5945414                Uncentered R2 =   0.1660
Residual SS             =  661.8656895                Root MSE      =    .3996

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        xpers |   .4692455   .1412875     3.32   0.001      .192327     .746164
           lt |   .0328384   .0222809     1.47   0.141    -.0108313    .0765081
       loggdp |  -.4913246   .0963406    -5.10   0.000    -.6801488   -.3025005
        lpopl |   .4515443   .2977704     1.52   0.129     -.132075    1.035164
       civwar |   .3530194   .0655141     5.39   0.000     .2246141    .4814247
       intwar |   .3919459   .1069636     3.66   0.000     .1823012    .6015907
        mean5 |   .1732238   .0356351     4.86   0.000     .1033803    .2430672
seniorofficer |   .1969414   .0939401     2.10   0.036     .0128223    .3810606
juniorofficer |  -.1057831   .1499766    -0.71   0.481    -.3997319    .1881657
 institutions |  -.1834386   .0704791    -2.60   0.009    -.3215751   -.0453022
   mpartyelec |  -.0217339   .0275477    -0.79   0.430    -.0757264    .0322585
-------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl civwar intwar mean5 seniorofficer
                      juniorofficer institutions mpartyelec
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_207 _Igwf_casei_208 _Igwf_casei_209
                      _Igwf_casei_210 _Igwf_casei_211 _Igwf_casei_212
                      _Igwf_casei_213 _Igwf_casei_214 _Igwf_casei_215
                      _Igwf_casei_216 _Igwf_casei_217 _Igwf_casei_218
                      _Igwf_casei_219 _Igwf_casei_220 _Igwf_casei_221
                      _Igwf_casei_222 _Igwf_casei_223 _Igwf_casei_224
                      _Igwf_casei_226 _Igwf_casei_227 _Igwf_casei_228
                      _Igwf_casei_229 _Igwf_casei_230 _Igwf_casei_231
                      _Igwf_casei_232 _Igwf_casei_233 _Igwf_casei_234
                      _Igwf_casei_235 _Igwf_casei_236 _Igwf_casei_237
                      _Igwf_casei_238 _Igwf_casei_242 _Igwf_casei_243
                      _Igwf_casei_244 _Igwf_casei_245 _Igwf_casei_246
                      _Igwf_casei_247 _Igwf_casei_248 _Igwf_casei_250
                      _Igwf_casei_251 _Igwf_casei_252 _Igwf_casei_253
                      _Igwf_casei_254 _Igwf_casei_255 _Igwf_casei_256
                      _Igwf_casei_258 _Igwf_casei_259 _Igwf_casei_260
                      _Igwf_casei_261 _Igwf_casei_262 _Igwf_casei_263
                      _Igwf_casei_264 _Igwf_casei_265 _Igwf_casei_266
                      _Igwf_casei_267 _Igwf_casei_268 _Igwf_casei_269
                      _Igwf_casei_270 _Igwf_casei_271 _Igwf_casei_272
                      _Igwf_casei_273 _Igwf_casei_274 _Igwf_casei_275
                      _Igwf_casei_276 _Igwf_casei_277 _Igwf_casei_278
                      _Igwf_casei_279 _Igwf_casei_280 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_225
------------------------------------------------------------------------------

.                         est store t4

.                 * add coups to specification *
.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $conflictvar $
> leadervar institution coupA coupS, ///
>                         cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_225

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 458               Number of obs =     4144
                                                      F( 12,   457) =    14.49
                                                      Prob > F      =   0.0000
Total (centered) SS     =  793.5945414                Centered R2   =   0.1677
Total (uncentered) SS   =  793.5945414                Uncentered R2 =   0.1677
Residual SS             =  660.5114705                Root MSE      =    .3992

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        xpers |   .4757766   .1405791     3.38   0.001     .2002465    .7513066
           lt |   .0315843    .022241     1.42   0.156    -.0120072    .0751758
       loggdp |  -.4863499   .0963894    -5.05   0.000    -.6752697   -.2974301
        lpopl |    .467038   .2965378     1.57   0.115    -.1141654    1.048241
       civwar |    .351185   .0652887     5.38   0.000     .2232216    .4791484
       intwar |   .3876184   .1071618     3.62   0.000     .1775852    .5976516
        mean5 |    .169341   .0355367     4.77   0.000     .0996903    .2389918
seniorofficer |   .1947178   .0938049     2.08   0.038     .0108635     .378572
juniorofficer |  -.0913437   .1501039    -0.61   0.543    -.3855419    .2028545
 institutions |   -.183814   .0713888    -2.57   0.010    -.3237334   -.0438946
        coupA |   .0757165   .0380184     1.99   0.046     .0012017    .1502312
        coupS |   .0936843   .0493385     1.90   0.058    -.0030175     .190386
-------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl civwar intwar mean5 seniorofficer
                      juniorofficer institutions coupA coupS
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_207 _Igwf_casei_208 _Igwf_casei_209
                      _Igwf_casei_210 _Igwf_casei_211 _Igwf_casei_212
                      _Igwf_casei_213 _Igwf_casei_214 _Igwf_casei_215
                      _Igwf_casei_216 _Igwf_casei_217 _Igwf_casei_218
                      _Igwf_casei_219 _Igwf_casei_220 _Igwf_casei_221
                      _Igwf_casei_222 _Igwf_casei_223 _Igwf_casei_224
                      _Igwf_casei_226 _Igwf_casei_227 _Igwf_casei_228
                      _Igwf_casei_229 _Igwf_casei_230 _Igwf_casei_231
                      _Igwf_casei_232 _Igwf_casei_233 _Igwf_casei_234
                      _Igwf_casei_235 _Igwf_casei_236 _Igwf_casei_237
                      _Igwf_casei_238 _Igwf_casei_242 _Igwf_casei_243
                      _Igwf_casei_244 _Igwf_casei_245 _Igwf_casei_246
                      _Igwf_casei_247 _Igwf_casei_248 _Igwf_casei_250
                      _Igwf_casei_251 _Igwf_casei_252 _Igwf_casei_253
                      _Igwf_casei_254 _Igwf_casei_255 _Igwf_casei_256
                      _Igwf_casei_258 _Igwf_casei_259 _Igwf_casei_260
                      _Igwf_casei_261 _Igwf_casei_262 _Igwf_casei_263
                      _Igwf_casei_264 _Igwf_casei_265 _Igwf_casei_266
                      _Igwf_casei_267 _Igwf_casei_268 _Igwf_casei_269
                      _Igwf_casei_270 _Igwf_casei_271 _Igwf_casei_272
                      _Igwf_casei_273 _Igwf_casei_274 _Igwf_casei_275
                      _Igwf_casei_276 _Igwf_casei_277 _Igwf_casei_278
                      _Igwf_casei_279 _Igwf_casei_280 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_225
------------------------------------------------------------------------------

.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $conflictvar $
> leadervar institution coup012, ///
>                         cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_225

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 458               Number of obs =     4144
                                                      F( 11,   457) =    15.82
                                                      Prob > F      =   0.0000
Total (centered) SS     =  793.5945414                Centered R2   =   0.1678
Total (uncentered) SS   =  793.5945414                Uncentered R2 =   0.1678
Residual SS             =  660.3943107                Root MSE      =    .3992

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        xpers |   .4692289   .1410237     3.33   0.001     .1928276    .7456303
           lt |   .0336167   .0221995     1.51   0.130    -.0098935    .0771268
       loggdp |  -.4849191   .0965256    -5.02   0.000    -.6741057   -.2957324
        lpopl |   .4753959    .296936     1.60   0.109    -.1065879     1.05738
       civwar |    .352926   .0658904     5.36   0.000     .2237832    .4820688
       intwar |   .3901311   .1061187     3.68   0.000     .1821424    .5981199
        mean5 |   .1696159   .0355875     4.77   0.000     .0998657    .2393662
seniorofficer |   .1954271    .093909     2.08   0.037     .0113687    .3794854
juniorofficer |  -.0915324   .1507252    -0.61   0.544    -.3869484    .2038836
 institutions |   -.179192   .0714668    -2.51   0.012    -.3192644   -.0391196
      coup012 |   .0762344   .0323756     2.35   0.019     .0127794    .1396894
-------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl civwar intwar mean5 seniorofficer
                      juniorofficer institutions coup012
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_20 _Igwf_casei_21 _Igwf_casei_22
                      _Igwf_casei_23 _Igwf_casei_24 _Igwf_casei_25
                      _Igwf_casei_26 _Igwf_casei_27 _Igwf_casei_28
                      _Igwf_casei_29 _Igwf_casei_30 _Igwf_casei_31
                      _Igwf_casei_35 _Igwf_casei_36 _Igwf_casei_37
                      _Igwf_casei_38 _Igwf_casei_39 _Igwf_casei_40
                      _Igwf_casei_41 _Igwf_casei_42 _Igwf_casei_43
                      _Igwf_casei_44 _Igwf_casei_45 _Igwf_casei_46
                      _Igwf_casei_47 _Igwf_casei_48 _Igwf_casei_49
                      _Igwf_casei_50 _Igwf_casei_51 _Igwf_casei_52
                      _Igwf_casei_53 _Igwf_casei_54 _Igwf_casei_55
                      _Igwf_casei_56 _Igwf_casei_57 _Igwf_casei_58
                      _Igwf_casei_59 _Igwf_casei_60 _Igwf_casei_61
                      _Igwf_casei_62 _Igwf_casei_63 _Igwf_casei_64
                      _Igwf_casei_65 _Igwf_casei_66 _Igwf_casei_67
                      _Igwf_casei_68 _Igwf_casei_70 _Igwf_casei_71
                      _Igwf_casei_72 _Igwf_casei_73 _Igwf_casei_74
                      _Igwf_casei_75 _Igwf_casei_76 _Igwf_casei_78
                      _Igwf_casei_79 _Igwf_casei_80 _Igwf_casei_81
                      _Igwf_casei_82 _Igwf_casei_83 _Igwf_casei_85
                      _Igwf_casei_86 _Igwf_casei_87 _Igwf_casei_89
                      _Igwf_casei_91 _Igwf_casei_92 _Igwf_casei_93
                      _Igwf_casei_94 _Igwf_casei_95 _Igwf_casei_96
                      _Igwf_casei_97 _Igwf_casei_98 _Igwf_casei_99
                      _Igwf_casei_100 _Igwf_casei_101 _Igwf_casei_102
                      _Igwf_casei_103 _Igwf_casei_104 _Igwf_casei_105
                      _Igwf_casei_106 _Igwf_casei_107 _Igwf_casei_108
                      _Igwf_casei_109 _Igwf_casei_110 _Igwf_casei_111
                      _Igwf_casei_112 _Igwf_casei_113 _Igwf_casei_114
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_117
                      _Igwf_casei_118 _Igwf_casei_119 _Igwf_casei_121
                      _Igwf_casei_122 _Igwf_casei_123 _Igwf_casei_124
                      _Igwf_casei_125 _Igwf_casei_126 _Igwf_casei_127
                      _Igwf_casei_128 _Igwf_casei_129 _Igwf_casei_130
                      _Igwf_casei_131 _Igwf_casei_132 _Igwf_casei_133
                      _Igwf_casei_134 _Igwf_casei_135 _Igwf_casei_136
                      _Igwf_casei_137 _Igwf_casei_138 _Igwf_casei_139
                      _Igwf_casei_140 _Igwf_casei_141 _Igwf_casei_142
                      _Igwf_casei_143 _Igwf_casei_144 _Igwf_casei_145
                      _Igwf_casei_146 _Igwf_casei_147 _Igwf_casei_148
                      _Igwf_casei_149 _Igwf_casei_150 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_162 _Igwf_casei_163
                      _Igwf_casei_164 _Igwf_casei_165 _Igwf_casei_166
                      _Igwf_casei_167 _Igwf_casei_168 _Igwf_casei_169
                      _Igwf_casei_170 _Igwf_casei_171 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_182 _Igwf_casei_184 _Igwf_casei_185
                      _Igwf_casei_186 _Igwf_casei_187 _Igwf_casei_188
                      _Igwf_casei_189 _Igwf_casei_190 _Igwf_casei_191
                      _Igwf_casei_192 _Igwf_casei_193 _Igwf_casei_194
                      _Igwf_casei_195 _Igwf_casei_196 _Igwf_casei_197
                      _Igwf_casei_198 _Igwf_casei_200 _Igwf_casei_201
                      _Igwf_casei_202 _Igwf_casei_205 _Igwf_casei_206
                      _Igwf_casei_207 _Igwf_casei_208 _Igwf_casei_209
                      _Igwf_casei_210 _Igwf_casei_211 _Igwf_casei_212
                      _Igwf_casei_213 _Igwf_casei_214 _Igwf_casei_215
                      _Igwf_casei_216 _Igwf_casei_217 _Igwf_casei_218
                      _Igwf_casei_219 _Igwf_casei_220 _Igwf_casei_221
                      _Igwf_casei_222 _Igwf_casei_223 _Igwf_casei_224
                      _Igwf_casei_226 _Igwf_casei_227 _Igwf_casei_228
                      _Igwf_casei_229 _Igwf_casei_230 _Igwf_casei_231
                      _Igwf_casei_232 _Igwf_casei_233 _Igwf_casei_234
                      _Igwf_casei_235 _Igwf_casei_236 _Igwf_casei_237
                      _Igwf_casei_238 _Igwf_casei_242 _Igwf_casei_243
                      _Igwf_casei_244 _Igwf_casei_245 _Igwf_casei_246
                      _Igwf_casei_247 _Igwf_casei_248 _Igwf_casei_250
                      _Igwf_casei_251 _Igwf_casei_252 _Igwf_casei_253
                      _Igwf_casei_254 _Igwf_casei_255 _Igwf_casei_256
                      _Igwf_casei_258 _Igwf_casei_259 _Igwf_casei_260
                      _Igwf_casei_261 _Igwf_casei_262 _Igwf_casei_263
                      _Igwf_casei_264 _Igwf_casei_265 _Igwf_casei_266
                      _Igwf_casei_267 _Igwf_casei_268 _Igwf_casei_269
                      _Igwf_casei_270 _Igwf_casei_271 _Igwf_casei_272
                      _Igwf_casei_273 _Igwf_casei_274 _Igwf_casei_275
                      _Igwf_casei_276 _Igwf_casei_277 _Igwf_casei_278
                      _Igwf_casei_279 _Igwf_casei_280 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      _Iyear_2010 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_225
------------------------------------------------------------------------------

.                         est store t5

.                 * add youth buldge to specification, only up to 2000 *
.                 xi: ivreg2 repression i.gwf_caseid i.year xpers lt loggdp lpopl $conflictvar $
> leadervar institution ythblgap, ///
>                         cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)
Warning - collinearities detected
Vars dropped:       _Igwf_casei_20 _Igwf_casei_58 _Igwf_casei_114
                    _Igwf_casei_117 _Igwf_casei_140 _Igwf_casei_150
                    _Igwf_casei_162 _Igwf_casei_170 _Igwf_casei_171
                    _Igwf_casei_182 _Igwf_casei_225 _Igwf_casei_242
                    _Igwf_casei_248 _Igwf_casei_269 _Igwf_casei_280 _Iyear_2001
                    _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                    _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 420               Number of obs =     3534
                                                      F( 11,   419) =     8.72
                                                      Prob > F      =   0.0000
Total (centered) SS     =  635.7250105                Centered R2   =   0.1633
Total (uncentered) SS   =  635.7250105                Uncentered R2 =   0.1633
Residual SS             =  531.8859893                Root MSE      =     .388

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        xpers |   .4682733    .151642     3.09   0.002     .1710606    .7654861
           lt |   .0229597   .0250443     0.92   0.359    -.0261262    .0720456
       loggdp |  -.4485747   .0995637    -4.51   0.000     -.643716   -.2534333
        lpopl |   .6138878   .2783847     2.21   0.027     .0682638    1.159512
       civwar |   .3431821   .0745568     4.60   0.000     .1970534    .4893108
       intwar |   .3512403   .1128615     3.11   0.002     .1300358    .5724448
        mean5 |   .1788886   .0395322     4.53   0.000     .1014069    .2563703
seniorofficer |   .1000755   .1793607     0.56   0.577    -.2514649    .4516159
juniorofficer |  -.1643714   .1971935    -0.83   0.405    -.5508637    .2221208
 institutions |  -.1713344   .0718444    -2.38   0.017    -.3121467    -.030522
     ythblgap |   .1269796   .0657082     1.93   0.053    -.0018061    .2557654
-------------------------------------------------------------------------------
Included instruments: xpers lt loggdp lpopl civwar intwar mean5 seniorofficer
                      juniorofficer institutions ythblgap
Partialled-out:       _Igwf_casei_2 _Igwf_casei_3 _Igwf_casei_4 _Igwf_casei_5
                      _Igwf_casei_6 _Igwf_casei_7 _Igwf_casei_8 _Igwf_casei_9
                      _Igwf_casei_11 _Igwf_casei_12 _Igwf_casei_13
                      _Igwf_casei_14 _Igwf_casei_15 _Igwf_casei_16
                      _Igwf_casei_17 _Igwf_casei_18 _Igwf_casei_19
                      _Igwf_casei_21 _Igwf_casei_22 _Igwf_casei_23
                      _Igwf_casei_24 _Igwf_casei_25 _Igwf_casei_26
                      _Igwf_casei_27 _Igwf_casei_28 _Igwf_casei_29
                      _Igwf_casei_30 _Igwf_casei_31 _Igwf_casei_35
                      _Igwf_casei_36 _Igwf_casei_37 _Igwf_casei_38
                      _Igwf_casei_39 _Igwf_casei_40 _Igwf_casei_41
                      _Igwf_casei_42 _Igwf_casei_43 _Igwf_casei_44
                      _Igwf_casei_45 _Igwf_casei_46 _Igwf_casei_47
                      _Igwf_casei_48 _Igwf_casei_49 _Igwf_casei_50
                      _Igwf_casei_51 _Igwf_casei_52 _Igwf_casei_53
                      _Igwf_casei_54 _Igwf_casei_55 _Igwf_casei_56
                      _Igwf_casei_57 _Igwf_casei_59 _Igwf_casei_60
                      _Igwf_casei_61 _Igwf_casei_62 _Igwf_casei_63
                      _Igwf_casei_64 _Igwf_casei_65 _Igwf_casei_66
                      _Igwf_casei_67 _Igwf_casei_68 _Igwf_casei_70
                      _Igwf_casei_71 _Igwf_casei_72 _Igwf_casei_73
                      _Igwf_casei_74 _Igwf_casei_75 _Igwf_casei_76
                      _Igwf_casei_78 _Igwf_casei_79 _Igwf_casei_80
                      _Igwf_casei_81 _Igwf_casei_82 _Igwf_casei_83
                      _Igwf_casei_85 _Igwf_casei_86 _Igwf_casei_87
                      _Igwf_casei_89 _Igwf_casei_91 _Igwf_casei_92
                      _Igwf_casei_93 _Igwf_casei_94 _Igwf_casei_95
                      _Igwf_casei_96 _Igwf_casei_97 _Igwf_casei_98
                      _Igwf_casei_99 _Igwf_casei_100 _Igwf_casei_101
                      _Igwf_casei_102 _Igwf_casei_103 _Igwf_casei_104
                      _Igwf_casei_105 _Igwf_casei_106 _Igwf_casei_107
                      _Igwf_casei_108 _Igwf_casei_109 _Igwf_casei_110
                      _Igwf_casei_111 _Igwf_casei_112 _Igwf_casei_113
                      _Igwf_casei_115 _Igwf_casei_116 _Igwf_casei_118
                      _Igwf_casei_119 _Igwf_casei_121 _Igwf_casei_122
                      _Igwf_casei_123 _Igwf_casei_124 _Igwf_casei_125
                      _Igwf_casei_126 _Igwf_casei_127 _Igwf_casei_128
                      _Igwf_casei_129 _Igwf_casei_130 _Igwf_casei_131
                      _Igwf_casei_132 _Igwf_casei_133 _Igwf_casei_134
                      _Igwf_casei_135 _Igwf_casei_136 _Igwf_casei_137
                      _Igwf_casei_138 _Igwf_casei_139 _Igwf_casei_141
                      _Igwf_casei_142 _Igwf_casei_143 _Igwf_casei_144
                      _Igwf_casei_145 _Igwf_casei_146 _Igwf_casei_147
                      _Igwf_casei_148 _Igwf_casei_149 _Igwf_casei_151
                      _Igwf_casei_152 _Igwf_casei_153 _Igwf_casei_154
                      _Igwf_casei_155 _Igwf_casei_156 _Igwf_casei_157
                      _Igwf_casei_158 _Igwf_casei_159 _Igwf_casei_160
                      _Igwf_casei_161 _Igwf_casei_163 _Igwf_casei_164
                      _Igwf_casei_165 _Igwf_casei_166 _Igwf_casei_167
                      _Igwf_casei_168 _Igwf_casei_169 _Igwf_casei_172
                      _Igwf_casei_173 _Igwf_casei_174 _Igwf_casei_175
                      _Igwf_casei_176 _Igwf_casei_177 _Igwf_casei_178
                      _Igwf_casei_179 _Igwf_casei_180 _Igwf_casei_181
                      _Igwf_casei_184 _Igwf_casei_185 _Igwf_casei_186
                      _Igwf_casei_187 _Igwf_casei_188 _Igwf_casei_189
                      _Igwf_casei_190 _Igwf_casei_191 _Igwf_casei_192
                      _Igwf_casei_193 _Igwf_casei_194 _Igwf_casei_195
                      _Igwf_casei_196 _Igwf_casei_197 _Igwf_casei_198
                      _Igwf_casei_200 _Igwf_casei_201 _Igwf_casei_202
                      _Igwf_casei_205 _Igwf_casei_206 _Igwf_casei_207
                      _Igwf_casei_208 _Igwf_casei_209 _Igwf_casei_210
                      _Igwf_casei_211 _Igwf_casei_212 _Igwf_casei_213
                      _Igwf_casei_214 _Igwf_casei_215 _Igwf_casei_216
                      _Igwf_casei_217 _Igwf_casei_218 _Igwf_casei_219
                      _Igwf_casei_220 _Igwf_casei_221 _Igwf_casei_222
                      _Igwf_casei_223 _Igwf_casei_224 _Igwf_casei_226
                      _Igwf_casei_227 _Igwf_casei_228 _Igwf_casei_229
                      _Igwf_casei_230 _Igwf_casei_231 _Igwf_casei_232
                      _Igwf_casei_233 _Igwf_casei_234 _Igwf_casei_235
                      _Igwf_casei_236 _Igwf_casei_237 _Igwf_casei_238
                      _Igwf_casei_243 _Igwf_casei_244 _Igwf_casei_245
                      _Igwf_casei_246 _Igwf_casei_247 _Igwf_casei_250
                      _Igwf_casei_251 _Igwf_casei_252 _Igwf_casei_253
                      _Igwf_casei_254 _Igwf_casei_255 _Igwf_casei_256
                      _Igwf_casei_258 _Igwf_casei_259 _Igwf_casei_260
                      _Igwf_casei_261 _Igwf_casei_262 _Igwf_casei_263
                      _Igwf_casei_264 _Igwf_casei_265 _Igwf_casei_266
                      _Igwf_casei_267 _Igwf_casei_268 _Igwf_casei_270
                      _Igwf_casei_271 _Igwf_casei_272 _Igwf_casei_273
                      _Igwf_casei_274 _Igwf_casei_275 _Igwf_casei_276
                      _Igwf_casei_277 _Igwf_casei_278 _Igwf_casei_279
                      _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959
                      _Iyear_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963
                      _Iyear_1964 _Iyear_1965 _Iyear_1966 _Iyear_1967
                      _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971
                      _Iyear_1972 _Iyear_1973 _Iyear_1974 _Iyear_1975
                      _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1979
                      _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983
                      _Iyear_1984 _Iyear_1985 _Iyear_1986 _Iyear_1987
                      _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991
                      _Iyear_1992 _Iyear_1993 _Iyear_1994 _Iyear_1995
                      _Iyear_1996 _Iyear_1997 _Iyear_1998 _Iyear_1999
                      _Iyear_2000 _cons
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Igwf_casei_20 _Igwf_casei_58 _Igwf_casei_114
                      _Igwf_casei_117 _Igwf_casei_140 _Igwf_casei_150
                      _Igwf_casei_162 _Igwf_casei_170 _Igwf_casei_171
                      _Igwf_casei_182 _Igwf_casei_225 _Igwf_casei_242
                      _Igwf_casei_248 _Igwf_casei_269 _Igwf_casei_280
                      _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004
                      _Iyear_2005 _Iyear_2006 _Iyear_2007 _Iyear_2008
                      _Iyear_2009 _Iyear_2010
------------------------------------------------------------------------------

.                         est store t6

.                 xtset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1955 to 2010
                delta:  1 unit

.                 xtreg repression coldwar xpers lt loggdp lpopl $conflictvar $leadervar institu
> tion ythblgap, cluster(gwf_caseid)                

Random-effects GLS regression                   Number of obs     =      3,534
Group variable: gwf_caseid                      Number of groups  =        246

R-sq:                                           Obs per group:
     within  = 0.1579                                         min =          1
     between = 0.2843                                         avg =       14.4
     overall = 0.3005                                         max =         46

                                                Wald chi2(12)     =     134.78
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                            (Std. Err. adjusted for 246 clusters in gwf_caseid)
-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
      coldwar |   .2392073   .0803604     2.98   0.003     .0817039    .3967107
        xpers |   .4834864   .1532225     3.16   0.002     .1831758    .7837971
           lt |   .0203611   .0238138     0.86   0.393    -.0263131    .0670354
       loggdp |  -.3264773   .0917584    -3.56   0.000    -.5063204   -.1466342
        lpopl |    .352158   .0851944     4.13   0.000       .18518    .5191359
       civwar |   .4012571   .0767131     5.23   0.000     .2509022    .5516121
       intwar |   .4299241   .1241805     3.46   0.001     .1865348    .6733134
        mean5 |   .1554444   .0470394     3.30   0.001     .0632489    .2476399
seniorofficer |   .0484379   .1701993     0.28   0.776    -.2851465    .3820224
juniorofficer |  -.1944616   .1346744    -1.44   0.149    -.4584186    .0694955
 institutions |  -.2313456   .0792322    -2.92   0.004    -.3866378   -.0760533
     ythblgap |   .1647646   .0620238     2.66   0.008     .0432001    .2863291
        _cons |  -.1998467   .1165004    -1.72   0.086    -.4281833      .02849
--------------+----------------------------------------------------------------
      sigma_u |  .71238325
      sigma_e |  .41080065
          rho |  .75045042   (fraction of variance due to u_i)
-------------------------------------------------------------------------------

.                 label var repress "Repression"

.                 label var xpers "{bf:Personalism}"

.                 label var senior "Senior officer"

.                 label var junior "Junior officer"

.                 label var civwar "Civil conflict"

.                 label var intwar "Int'l conflict"

.                 label var mean5 "Protest"

.                 label var lt `""Leader time" "in power""'

.                 label var loggdp "GDP per capita"

.                 label var lpopl "Population"

.                 label var inst "Institutions"

.                 label var G_age "Leader age"

.                 label var nav_protestV `""Violent" "protest camp.""'

.                 label var nav_protestNV `""Non-violent" "protest camp.""'

.                 label var lji `""Judicial" "indep.""'

.                 label var mpartyelec `""Multiparty" "election""'

.                 label var coup012 "Reccent coup"

.                 label var ythblgap "Youth bulge"

.                 coefplot (t1, msym(d)) (t2, msym(t)) (t3, msym(oh)) (t4, msym(plus)) (t5, msym
> (P)) (t6, msym(T)), ///
>                 title("Additional covariates", size(medsmall)) drop(_cons  _I* D*) order(xpers
> ) xline(0) ///
>                 grid(glcolor(gs15)) mfcolor(white) xlabel(-1.5(.5)1) levels(95 90)  ///
>                 legend(off) ysize(10)  xtitle("Two-way fixed effects estimate", height(6)) ///
>                 note("90 (thin) and 95 (thick) percent confidence intervals", size(vsmall) pos
> (6))
(note:  named style P not found in class symbol, default attributes used)

.                 graph export "$dir\repression-additional-covariates.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\repression-additiona
> l-covariates.pdf written in PDF format)

.                 
.                 
.                 * change the cross-section unit to country/leader *
.                 xtset cow year
       panel variable:  cowcode (unbalanced)
        time variable:  year, 1955 to 2010, but with gaps
                delta:  1 unit

.                 qui:xtreg repression i.year xpers lt loggdp lpopl $conflictvar $leadervar inst
> itutions, re cluster(cow) 

.                 est store cs1

.                 qui:xtreg repression i.year xpers lt loggdp lpopl $conflictvar $leadervar inst
> itutions, fe cluster(cow) 

.                 est store cs2

.                 xtset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1955 to 2010
                delta:  1 unit

.                 qui:xtreg repression i.year xpers lt loggdp lpopl $conflictvar $leadervar inst
> itutions, re cluster(gwf_caseid)  

.                 est store cs3

.                 qui:xtreg repression i.year xpers lt loggdp lpopl $conflictvar $leadervar inst
> itutions, fe cluster(gwf_caseid)  

.                 est store cs4

.                 xtset gwf_leaderid year
       panel variable:  gwf_leaderid (unbalanced)
        time variable:  year, 1955 to 2010, but with gaps
                delta:  1 unit

.                 qui:xtreg repression i.year xpers lt loggdp lpopl $conflictvar $leadervar inst
> itutions, re cluster(gwf_leaderid)        

.                 est store cs5

.                 qui:xtreg repression i.year xpers lt loggdp lpopl $conflictvar  institutions, 
> fe cluster(gwf_leaderid)  

.                 est store cs6   

.                 label var repress "Repression"

.                 label var xpers "{bf:Personalism}"

.                 label var senior "Senior officer"

.                 label var junior "Junior officer"

.                 label var civwar "Civil conflict"

.                 label var intwar "Int'l conflict"

.                 label var mean5 "Protest"

.                 label var lt `""Leader time" "in power""'

.                 label var loggdp "GDP per capita"

.                 label var lpopl "Population"

.                 label var inst "Institutions"

.                 coefplot (cs1, msym(d)) (cs2, msym(t)) (cs3, msym(oh)) (cs4, msym(plus)) (cs5,
>  msym(P)) (cs6, msym(T)), ///
>                 title("Changing the cross-section unit", size(medsmall)) keep(xpers lt loggdp 
> lpopl $conflictvar $leadervar institutions) order(xpers) xline(0) ///
>                 grid(glcolor(gs15)) mfcolor(white) xlabel(-1.5(.5)1) levels(95 90)  ///
>                 legend(lab(3 "Country, RE") lab(6 "Country, FE") lab(9 "Case, RE") lab(12 "Cas
> e, FE") lab(15 "Leader, RE") lab(18 "Leader, FE") pos(6)col(3)ring(1)) ///
>                 ysize(8)  xtitle("Two-way fixed effects estimate", height(6)) ///
>                 note("90 (thin) and 95 (thick) percent confidence intervals", size(vsmall) pos
> (6))
(note:  named style P not found in class symbol, default attributes used)

.                 graph export "$dir\repression-change-cs-unit.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\repression-change-cs
> -unit.pdf written in PDF format)

.                 xtset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1955 to 2010
                delta:  1 unit

.                 
.                 * check that marginal effect is relatively constant across time *
.                 drop if year>2010
(0 observations deleted)

.                 interflex repression xpers year lt loggdp lpopl $conflictvar $leadervar instit
> utions , ///
>                         cluster(gwf_caseid) fe(gwf_caseid year) nbins(4) xr(1960 2000)  title(
> Calendar time in four equal bins) ///
>                         xlab(Year) ylab(Repression) dlab(Personalism) seed($seed) saving(time1
> )
p value of Wald test: 0.9974

.          
. 
.                 
. *****************
. * Lag DV models *
. *****************
.                 use temp,clear

.                 set seed $seed

.                 xtset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1950 to 2010
                delta:  1 unit

.                 gen lagrepress = l.repress
(271 missing values generated)

.                 * check for autocorrelation in Lag DV *
.                 xi: xtserial repress i.year lagrepress lt loggdp lpopl $conflictvar $leadervar
>  institutions xpers 
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
    F(  1,     206) =    517.391
           Prob > F =      0.0000

.                 xi: qui reg repress i.year l.repress lt loggdp lpopl $conflictvar $leadervar i
> nstitutions xpers,cluster(gwf_caseid)
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

.                 lincom xpers

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0295504   .0164805     1.79   0.074    -.0029139    .0620147
------------------------------------------------------------------------------

.                 xi: qui xtgls repress i.year l.repress lt loggdp lpopl $conflictvar $leadervar
>  institutions xpers,corr(ar1) force
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

.                 lincom xpers

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0326136   .0154612     2.11   0.035     .0023103    .0629169
------------------------------------------------------------------------------

.                 xi: qui xtgls repress i.year l.repress lt loggdp lpopl $conflictvar $leadervar
>  institutions xpers,corr(psar1) force
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

.                 lincom xpers

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0342102   .0130653     2.62   0.009     .0086026    .0598178
------------------------------------------------------------------------------

.                 xi: qui xtpcse repress i.year l.repress lt loggdp lpopl $conflictvar $leaderva
> r institutions xpers,corr(ar1) het
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

.                 lincom xpers

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0339346   .0156621     2.17   0.030     .0032375    .0646316
------------------------------------------------------------------------------

.                 xi: qui xtpcse repress i.year l.repress lt loggdp lpopl $conflictvar $leaderva
> r institutions xpers,corr(psar1) het
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

.                 lincom xpers

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0342616   .0141752     2.42   0.016     .0064788    .0620445
------------------------------------------------------------------------------

.                 
.                 * Full ECM, calculating the LRM *
.                 xi:ivreg2 repress (d.repress=l.repress) i.year  d.xpers xpers,rob bw(3) 
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)
Warning - collinearities detected
Vars dropped:       _Iyear_2010

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and autocorrelation
  kernel=Bartlett; bandwidth=     3
  time variable (t):  year
  group variable (i): gwf_caseid

                                                      Number of obs =     4161
                                                      F( 62,  4098) =     0.30
                                                      Prob > F      =   1.0000
Total (centered) SS     =  4152.623114                Centered R2   = -1.4e+02
Total (uncentered) SS   =  4152.662329                Uncentered R2 = -1.4e+02
Residual SS             =  597935.6955                Root MSE      =    11.99

------------------------------------------------------------------------------
             |               Robust
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  repression |
         D1. |   -81.9955   20.25749    -4.05   0.000    -121.6994   -42.29156
             |
 _Iyear_1951 |  -.0569499    1.62941    -0.03   0.972    -3.250535    3.136635
 _Iyear_1952 |  -.6620407   1.670205    -0.40   0.692    -3.935582    2.611501
 _Iyear_1953 |  -.7046787   1.665615    -0.42   0.672    -3.969224    2.559867
 _Iyear_1954 |   .5300233   1.741504     0.30   0.761    -2.883261    3.943308
 _Iyear_1955 |   1.080517   1.706099     0.63   0.527    -2.263375    4.424408
 _Iyear_1956 |   1.351536   1.839076     0.73   0.462    -2.252987    4.956059
 _Iyear_1957 |   2.007917    1.88229     1.07   0.286    -1.681303    5.697138
 _Iyear_1958 |   3.659662   2.236857     1.64   0.102    -.7244973    8.043822
 _Iyear_1959 |   2.569799     1.8666     1.38   0.169     -1.08867    6.228269
 _Iyear_1960 |   1.916724   1.785365     1.07   0.283    -1.582528    5.415976
 _Iyear_1961 |    2.09713   1.850962     1.13   0.257    -1.530689    5.724949
 _Iyear_1962 |   2.626078   1.877811     1.40   0.162    -1.054364    6.306521
 _Iyear_1963 |   1.817721   1.703296     1.07   0.286    -1.520678     5.15612
 _Iyear_1964 |   1.793388    1.84357     0.97   0.331    -1.819944     5.40672
 _Iyear_1965 |    2.65095   2.164964     1.22   0.221    -1.592302    6.894202
 _Iyear_1966 |   .7432152   1.886368     0.39   0.694    -2.953999    4.440429
 _Iyear_1967 |   .4275399   1.650156     0.26   0.796    -2.806707    3.661787
 _Iyear_1968 |    .479079   1.576181     0.30   0.761    -2.610178    3.568336
 _Iyear_1969 |   .8170754    1.62404     0.50   0.615    -2.365984    4.000135
 _Iyear_1970 |   1.165887   1.628913     0.72   0.474    -2.026723    4.358497
 _Iyear_1971 |   1.073729    1.74508     0.62   0.538    -2.346565    4.494024
 _Iyear_1972 |   .3657593   1.588147     0.23   0.818    -2.746952     3.47847
 _Iyear_1973 |    .197978   1.819182     0.11   0.913    -3.367553    3.763509
 _Iyear_1974 |   1.382284    1.86206     0.74   0.458    -2.267287    5.031854
 _Iyear_1975 |   2.792961   1.901726     1.47   0.142    -.9343531    6.520276
 _Iyear_1976 |   2.428766   2.001034     1.21   0.225    -1.493188    6.350719
 _Iyear_1977 |   3.038085   2.014529     1.51   0.132    -.9103198    6.986489
 _Iyear_1978 |   2.091257   2.005597     1.04   0.297     -1.83964    6.022154
 _Iyear_1979 |   .5423226   1.833815     0.30   0.767    -3.051889    4.136534
 _Iyear_1980 |   .3143668   1.984206     0.16   0.874    -3.574605    4.203339
 _Iyear_1981 |   .8733349   1.979679     0.44   0.659    -3.006765    4.753435
 _Iyear_1982 |  -.3810745   1.933901    -0.20   0.844    -4.171451    3.409302
 _Iyear_1983 |  -1.092158   2.102838    -0.52   0.604    -5.213644    3.029328
 _Iyear_1984 |  -1.663467   1.936406    -0.86   0.390    -5.458752    2.131818
 _Iyear_1985 |  -1.069428   1.858655    -0.58   0.565    -4.712325    2.573469
 _Iyear_1986 |  -2.254676   1.998672    -1.13   0.259    -6.172001    1.662648
 _Iyear_1987 |  -2.375693   2.029982    -1.17   0.242    -6.354386    1.602999
 _Iyear_1988 |   -1.81616   2.288681    -0.79   0.427    -6.301893    2.669574
 _Iyear_1989 |    -1.3277   2.420454    -0.55   0.583    -6.071703    3.416303
 _Iyear_1990 |  -.4752759   2.818648    -0.17   0.866    -5.999723    5.049172
 _Iyear_1991 |   -2.27925   2.652784    -0.86   0.390    -7.478611    2.920111
 _Iyear_1992 |  -3.171811   2.619856    -1.21   0.226    -8.306635    1.963013
 _Iyear_1993 |   -2.68112   2.428924    -1.10   0.270    -7.441723    2.079483
 _Iyear_1994 |  -2.588744     2.4528    -1.06   0.291    -7.396144    2.218655
 _Iyear_1995 |  -2.278978   2.330986    -0.98   0.328    -6.847626     2.28967
 _Iyear_1996 |  -.6820284   1.994541    -0.34   0.732    -4.591257      3.2272
 _Iyear_1997 |   1.608196   2.113108     0.76   0.447     -2.53342    5.749812
 _Iyear_1998 |  -.8614408   2.259284    -0.38   0.703    -5.289556    3.566675
 _Iyear_1999 |  -3.821802   2.297795    -1.66   0.096    -8.325397    .6817924
 _Iyear_2000 |  -4.155374   2.537112    -1.64   0.101    -9.128023    .8172749
 _Iyear_2001 |  -2.733707   2.054608    -1.33   0.183    -6.760664     1.29325
 _Iyear_2002 |  -1.941919   2.256319    -0.86   0.389    -6.364224    2.480386
 _Iyear_2003 |  -1.769309   2.263295    -0.78   0.434    -6.205286    2.666668
 _Iyear_2004 |   .3142926   2.161373     0.15   0.884    -3.921921    4.550506
 _Iyear_2005 |   .1831504   2.110443     0.09   0.931    -3.953242    4.319543
 _Iyear_2006 |  -1.161735   2.413298    -0.48   0.630    -5.891712    3.568241
 _Iyear_2007 |  -1.033446   2.316504    -0.45   0.656     -5.57371    3.506817
 _Iyear_2008 |  -2.838484   2.396871    -1.18   0.236    -7.536266    1.859297
 _Iyear_2009 |   -2.65793   1.831562    -1.45   0.147    -6.247726    .9318666
             |
       xpers |
         D1. |  -.6022048   2.212504    -0.27   0.785    -4.938634    3.734224
         --. |   2.050306   1.022987     2.00   0.045      .045288    4.055323
       _cons |  -.8839655   1.480674    -0.60   0.551    -3.786034    2.018103
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             16.516
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         16.531
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         D.repression
Included instruments: _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958
                      _Iyear_1959 _Iyear_1960 _Iyear_1961 _Iyear_1962
                      _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1966
                      _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970
                      _Iyear_1971 _Iyear_1972 _Iyear_1973 _Iyear_1974
                      _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978
                      _Iyear_1979 _Iyear_1980 _Iyear_1981 _Iyear_1982
                      _Iyear_1983 _Iyear_1984 _Iyear_1985 _Iyear_1986
                      _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990
                      _Iyear_1991 _Iyear_1992 _Iyear_1993 _Iyear_1994
                      _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998
                      _Iyear_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002
                      _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                      _Iyear_2007 _Iyear_2008 _Iyear_2009 D.xpers xpers
Excluded instruments: L.repression
Dropped collinear:    _Iyear_2010
------------------------------------------------------------------------------

.                 est store ecm1a

.                 xi:ivreg2 repress (d.repress=l.repress) i.year d.lt lt d.loggdp loggdp d.lpopl
>  lpopl d.xpers xpers,rob bw(3) 
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)
Warning - collinearities detected
Vars dropped:       _Iyear_2010

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and autocorrelation
  kernel=Bartlett; bandwidth=     3
  time variable (t):  year
  group variable (i): gwf_caseid

                                                      Number of obs =     4108
                                                      F( 68,  4039) =     0.70
                                                      Prob > F      =   0.9704
Total (centered) SS     =  4058.298168                Centered R2   = -57.1027
Total (uncentered) SS   =  4058.673057                Uncentered R2 = -57.0973
Residual SS             =  235798.0139                Root MSE      =    7.576

------------------------------------------------------------------------------
             |               Robust
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  repression |
         D1. |  -51.62524   9.920842    -5.20   0.000    -71.06974   -32.18075
             |
 _Iyear_1951 |  -.2355614   1.122432    -0.21   0.834    -2.435487    1.964364
 _Iyear_1952 |  -.6339753   1.116672    -0.57   0.570    -2.822611    1.554661
 _Iyear_1953 |  -.7247633   1.109702    -0.65   0.514    -2.899738    1.450212
 _Iyear_1954 |   .0841337   1.137724     0.07   0.941    -2.145763    2.314031
 _Iyear_1955 |   .4409952   1.124161     0.39   0.695     -1.76232    2.644311
 _Iyear_1956 |     .72975   1.210311     0.60   0.547    -1.642415    3.101915
 _Iyear_1957 |   1.219214   1.204266     1.01   0.311    -1.141104    3.579532
 _Iyear_1958 |   2.166868   1.382326     1.57   0.117    -.5424401    4.876177
 _Iyear_1959 |   1.543606   1.191738     1.30   0.195    -.7921575     3.87937
 _Iyear_1960 |   1.042308   1.151267     0.91   0.365    -1.214134    3.298751
 _Iyear_1961 |   1.324879   1.190838     1.11   0.266    -1.009121    3.658879
 _Iyear_1962 |   1.513315   1.198211     1.26   0.207    -.8351361    3.861766
 _Iyear_1963 |   1.086937   1.096641     0.99   0.322     -1.06244    3.236314
 _Iyear_1964 |   1.048375   1.161757     0.90   0.367    -1.228626    3.325376
 _Iyear_1965 |   1.689252   1.343265     1.26   0.209    -.9434982    4.322002
 _Iyear_1966 |   .5007735   1.215726     0.41   0.680    -1.882005    2.883552
 _Iyear_1967 |   .2644816   1.083358     0.24   0.807     -1.85886    2.387824
 _Iyear_1968 |   .2443348   1.039157     0.24   0.814    -1.792375    2.281044
 _Iyear_1969 |   .5278149    1.05472     0.50   0.617    -1.539399    2.595029
 _Iyear_1970 |   .8280303   1.060259     0.78   0.435    -1.250038    2.906099
 _Iyear_1971 |   .7888116   1.125542     0.70   0.483    -1.417211    2.994834
 _Iyear_1972 |    .380351     1.0377     0.37   0.714    -1.653503    2.414205
 _Iyear_1973 |    .305921   1.181732     0.26   0.796    -2.010232    2.622074
 _Iyear_1974 |   .9036569   1.186612     0.76   0.446    -1.422061    3.229375
 _Iyear_1975 |   1.899759   1.191173     1.59   0.111    -.4348965    4.234414
 _Iyear_1976 |   1.648623   1.258393     1.31   0.190    -.8177827    4.115029
 _Iyear_1977 |   2.081911   1.257029     1.66   0.098    -.3818199    4.545642
 _Iyear_1978 |    1.47201    1.28393     1.15   0.252    -1.044446    3.988466
 _Iyear_1979 |   .5217204   1.199023     0.44   0.663    -1.828322    2.871763
 _Iyear_1980 |   .3871654    1.29522     0.30   0.765    -2.151418    2.925749
 _Iyear_1981 |   .8851499   1.278329     0.69   0.489    -1.620328    3.390628
 _Iyear_1982 |  -.0161912   1.241283    -0.01   0.990    -2.449061    2.416679
 _Iyear_1983 |  -.4134919   1.339718    -0.31   0.758    -3.039291    2.212307
 _Iyear_1984 |  -.9110098   1.234381    -0.74   0.460    -3.330352    1.508332
 _Iyear_1985 |  -.4202273   1.178682    -0.36   0.721    -2.730401    1.889947
 _Iyear_1986 |   -1.21241   1.265674    -0.96   0.338    -3.693086    1.268265
 _Iyear_1987 |  -1.590727   1.277951    -1.24   0.213    -4.095466     .914012
 _Iyear_1988 |  -1.148818   1.459695    -0.79   0.431    -4.009768    1.712132
 _Iyear_1989 |  -.7913526   1.539118    -0.51   0.607    -3.807968    2.225263
 _Iyear_1990 |   .1977445   1.823759     0.11   0.914    -3.376758    3.772247
 _Iyear_1991 |  -1.151694   1.689087    -0.68   0.495    -4.462244    2.158856
 _Iyear_1992 |   -2.01509   1.657127    -1.22   0.224       -5.263    1.232819
 _Iyear_1993 |  -1.791137    1.54598    -1.16   0.247    -4.821202    1.238928
 _Iyear_1994 |  -1.685855   1.534899    -1.10   0.272    -4.694201    1.322491
 _Iyear_1995 |  -1.526144   1.483473    -1.03   0.304    -4.433698     1.38141
 _Iyear_1996 |  -.5953204   1.279717    -0.47   0.642     -3.10352    1.912879
 _Iyear_1997 |    1.03559   1.360246     0.76   0.446    -1.630443    3.701623
 _Iyear_1998 |   -.547884   1.449828    -0.38   0.706    -3.389495    2.293727
 _Iyear_1999 |  -2.462914   1.404196    -1.75   0.079    -5.215088    .2892599
 _Iyear_2000 |  -2.452621   1.546376    -1.59   0.113    -5.483461    .5782199
 _Iyear_2001 |  -1.761157   1.294564    -1.36   0.174    -4.298456    .7761419
 _Iyear_2002 |  -1.148962   1.427394    -0.80   0.421    -3.946602    1.648678
 _Iyear_2003 |  -1.037264   1.425469    -0.73   0.467    -3.831132    1.756604
 _Iyear_2004 |    .291757   1.390635     0.21   0.834    -2.433838    3.017352
 _Iyear_2005 |   .2833472   1.358237     0.21   0.835    -2.378749    2.945444
 _Iyear_2006 |  -.3814123   1.529509    -0.25   0.803    -3.379195     2.61637
 _Iyear_2007 |  -.2795743   1.474991    -0.19   0.850    -3.170504    2.611355
 _Iyear_2008 |  -1.608963   1.513503    -1.06   0.288    -4.575375    1.357448
 _Iyear_2009 |  -1.591993   1.164659    -1.37   0.172    -3.874684    .6906968
             |
          lt |
         D1. |   .6138035   .2491838     2.46   0.014     .1254122    1.102195
         --. |  -.1811194   .1930285    -0.94   0.348    -.5594483    .1972094
             |
      loggdp |
         D1. |  -2.751913   1.922728    -1.43   0.152     -6.52039    1.016564
         --. |  -.4859067   .1724941    -2.82   0.005     -.823989   -.1478244
             |
       lpopl |
         D1. |   7.406768   8.969811     0.83   0.409    -10.17374    24.98727
         --. |   .4207572   .1607844     2.62   0.009     .1056255    .7358888
             |
       xpers |
         D1. |  -1.930717   1.523678    -1.27   0.205    -4.917071    1.055637
         --. |   1.457721   .6869736     2.12   0.034     .1112777    2.804165
       _cons |   -.747012   .9770987    -0.76   0.445     -2.66209    1.168066
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             26.881
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         27.665
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         D.repression
Included instruments: _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958
                      _Iyear_1959 _Iyear_1960 _Iyear_1961 _Iyear_1962
                      _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1966
                      _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970
                      _Iyear_1971 _Iyear_1972 _Iyear_1973 _Iyear_1974
                      _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978
                      _Iyear_1979 _Iyear_1980 _Iyear_1981 _Iyear_1982
                      _Iyear_1983 _Iyear_1984 _Iyear_1985 _Iyear_1986
                      _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990
                      _Iyear_1991 _Iyear_1992 _Iyear_1993 _Iyear_1994
                      _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998
                      _Iyear_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002
                      _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                      _Iyear_2007 _Iyear_2008 _Iyear_2009 D.lt lt D.loggdp
                      loggdp D.lpopl lpopl D.xpers xpers
Excluded instruments: L.repression
Dropped collinear:    _Iyear_2010
------------------------------------------------------------------------------

.                 est store ecm1b

.                 xi:ivreg2 repress (d.repress=l.repress) i.year d.lt lt d.loggdp loggdp d.lpopl
>  lpopl d.civwar civwar ///
>                         d.intwar intwar d.mean5 mean5 d.xpers xpers,rob bw(3) 
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)
Warning - collinearities detected
Vars dropped:       _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954 _Iyear_1955
                    _Iyear_2010

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and autocorrelation
  kernel=Bartlett; bandwidth=     3
  time variable (t):  year
  group variable (i): gwf_caseid

                                                      Number of obs =     3884
                                                      F( 69,  3814) =     0.93
                                                      Prob > F      =   0.6304
Total (centered) SS     =  3867.293325                Centered R2   = -40.8268
Total (uncentered) SS   =  3869.076277                Uncentered R2 = -40.8075
Residual SS             =  161756.3176                Root MSE      =    6.453

------------------------------------------------------------------------------
             |               Robust
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  repression |
         D1. |   -43.5229   7.822447    -5.56   0.000    -58.85461   -28.19118
             |
 _Iyear_1956 |   .0539724   1.006536     0.05   0.957    -1.918801    2.026746
 _Iyear_1957 |   .7723033    1.00437     0.77   0.442    -1.196227    2.740833
 _Iyear_1958 |    1.61269   1.152921     1.40   0.162    -.6469937    3.872373
 _Iyear_1959 |   1.081246   .9745077     1.11   0.267    -.8287536    2.991246
 _Iyear_1960 |   .6845896   .9556663     0.72   0.474    -1.188482    2.557661
 _Iyear_1961 |   1.059092   .9996906     1.06   0.289    -.9002656     3.01845
 _Iyear_1962 |   1.230899   1.004674     1.23   0.221    -.7382251    3.200023
 _Iyear_1963 |   .8225129   .9191404     0.89   0.371    -.9789691    2.623995
 _Iyear_1964 |   .9091665    .979725     0.93   0.353    -1.011059    2.829392
 _Iyear_1965 |   1.511376   1.110533     1.36   0.174     -.665228     3.68798
 _Iyear_1966 |   .4396411   1.002486     0.44   0.661    -1.525195    2.404477
 _Iyear_1967 |   .1217983   .9061525     0.13   0.893    -1.654228    1.897825
 _Iyear_1968 |   .2484106   .8753126     0.28   0.777    -1.467171    1.963992
 _Iyear_1969 |   .5231009   .8858471     0.59   0.555    -1.213128    2.259329
 _Iyear_1970 |   .8083398   .8837441     0.91   0.360    -.9237668    2.540446
 _Iyear_1971 |   .9505268   .9352748     1.02   0.309    -.8825781    2.783632
 _Iyear_1972 |    .502685    .863385     0.58   0.560    -1.189518    2.194889
 _Iyear_1973 |   .4513702   .9885105     0.46   0.648    -1.486075    2.388815
 _Iyear_1974 |   .8614518   .9878088     0.87   0.383    -1.074618    2.797522
 _Iyear_1975 |   1.823247   .9951835     1.83   0.067    -.1272766    3.773771
 _Iyear_1976 |   1.519393   1.059732     1.43   0.152    -.5576447     3.59643
 _Iyear_1977 |   1.843369   1.051361     1.75   0.080    -.2172608    3.903999
 _Iyear_1978 |   1.236009   1.074748     1.15   0.250    -.8704578    3.342476
 _Iyear_1979 |   .3644578   1.010479     0.36   0.718    -1.616044    2.344959
 _Iyear_1980 |   .3964792   1.079618     0.37   0.713    -1.719533    2.512492
 _Iyear_1981 |   .6954152   1.059068     0.66   0.511     -1.38032    2.771151
 _Iyear_1982 |  -.0966821   1.042751    -0.09   0.926    -2.140437    1.947073
 _Iyear_1983 |  -.2518095   1.121964    -0.22   0.822    -2.450819      1.9472
 _Iyear_1984 |   -.847289   1.044809    -0.81   0.417    -2.895078      1.2005
 _Iyear_1985 |  -.3805168    .994885    -0.38   0.702    -2.330456    1.569422
 _Iyear_1986 |  -1.247772   1.083129    -1.15   0.249    -3.370665    .8751223
 _Iyear_1987 |  -1.731607   1.101689    -1.57   0.116    -3.890878    .4276645
 _Iyear_1988 |  -1.466504   1.238806    -1.18   0.236     -3.89452    .9615107
 _Iyear_1989 |  -1.187208   1.316428    -0.90   0.367    -3.767359    1.392944
 _Iyear_1990 |  -.2941026   1.524392    -0.19   0.847    -3.281856    2.693651
 _Iyear_1991 |  -1.477724   1.405788    -1.05   0.293    -4.233018    1.277569
 _Iyear_1992 |  -1.773775   1.425402    -1.24   0.213    -4.567511    1.019962
 _Iyear_1993 |  -1.557599   1.317447    -1.18   0.237    -4.139748    1.024549
 _Iyear_1994 |   -1.39956    1.31177    -1.07   0.286    -3.970582    1.171461
 _Iyear_1995 |  -1.133481   1.240625    -0.91   0.361    -3.565061    1.298099
 _Iyear_1996 |  -.5087622   1.083215    -0.47   0.639    -2.631826    1.614301
 _Iyear_1997 |   .7526665   1.161149     0.65   0.517    -1.523145    3.028478
 _Iyear_1998 |  -.4055048   1.238966    -0.33   0.743    -2.833834    2.022824
 _Iyear_1999 |   -2.10452   1.181707    -1.78   0.075    -4.420623    .2115839
 _Iyear_2000 |  -2.065234   1.276602    -1.62   0.106    -4.567328    .4368598
 _Iyear_2001 |  -1.473014   1.083114    -1.36   0.174    -3.595878     .649849
 _Iyear_2002 |  -.8008057    1.20052    -0.67   0.505    -3.153782    1.552171
 _Iyear_2003 |  -.8980588   1.205125    -0.75   0.456     -3.26006    1.463943
 _Iyear_2004 |   .1171812   1.153214     0.10   0.919    -2.143076    2.377438
 _Iyear_2005 |   .3420338   1.150176     0.30   0.766     -1.91227    2.596337
 _Iyear_2006 |   .1171497   1.302422     0.09   0.928     -2.43555    2.669849
 _Iyear_2007 |  -.2628567   1.215957    -0.22   0.829    -2.646088    2.120375
 _Iyear_2008 |  -1.324031   1.283331    -1.03   0.302    -3.839314    1.191251
 _Iyear_2009 |  -1.301719   1.000795    -1.30   0.193    -3.263242    .6598037
             |
          lt |
         D1. |   .5633594   .2216371     2.54   0.011     .1289587    .9977601
         --. |  -.1553227   .1705817    -0.91   0.363    -.4896566    .1790113
             |
      loggdp |
         D1. |  -.9994798   1.710185    -0.58   0.559    -4.351382    2.352422
         --. |  -.6868946    .186057    -3.69   0.000     -1.05156   -.3222296
             |
       lpopl |
         D1. |   13.01777   10.91113     1.19   0.233    -8.367656     34.4032
         --. |  -.1532686   .2135218    -0.72   0.473    -.5717637    .2652264
             |
      civwar |
         D1. |   1.626012   .9905131     1.64   0.101    -.3153577    3.567382
         --. |  -.0638147   .8714636    -0.07   0.942    -1.771852    1.644223
             |
      intwar |
         D1. |   2.331872   1.178223     1.98   0.048     .0225961    4.641147
         --. |    1.15155    .908238     1.27   0.205    -.6285639    2.931664
             |
       mean5 |
         D1. |   2.696116   .8608728     3.13   0.002     1.008836    4.383395
         --. |    .679305   .1844085     3.68   0.000      .317871    1.040739
             |
       xpers |
         D1. |  -1.845469   1.367404    -1.35   0.177    -4.525532     .834594
         --. |     1.4729   .5999883     2.45   0.014     .2969448    2.648856
       _cons |  -.7762949   .8348764    -0.93   0.352    -2.412623    .8600327
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             30.649
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         31.811
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         D.repression
Included instruments: _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959
                      _Iyear_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963
                      _Iyear_1964 _Iyear_1965 _Iyear_1966 _Iyear_1967
                      _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971
                      _Iyear_1972 _Iyear_1973 _Iyear_1974 _Iyear_1975
                      _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1979
                      _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983
                      _Iyear_1984 _Iyear_1985 _Iyear_1986 _Iyear_1987
                      _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991
                      _Iyear_1992 _Iyear_1993 _Iyear_1994 _Iyear_1995
                      _Iyear_1996 _Iyear_1997 _Iyear_1998 _Iyear_1999
                      _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003
                      _Iyear_2004 _Iyear_2005 _Iyear_2006 _Iyear_2007
                      _Iyear_2008 _Iyear_2009 D.lt lt D.loggdp loggdp D.lpopl
                      lpopl D.civwar civwar D.intwar intwar D.mean5 mean5
                      D.xpers xpers
Excluded instruments: L.repression
Dropped collinear:    _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_2010
------------------------------------------------------------------------------

.                 est store ecm1c

.                 xi:ivreg2 repress (d.repress=l.repress) i.year d.lt lt d.loggdp loggdp d.lpopl
>  lpopl ///
>                         d.senior senior d.junior junior d.xpers xpers,rob bw(3) 
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)
Warning - collinearities detected
Vars dropped:       _Iyear_2010

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and autocorrelation
  kernel=Bartlett; bandwidth=     3
  time variable (t):  year
  group variable (i): gwf_caseid

                                                      Number of obs =     4108
                                                      F( 72,  4035) =     0.71
                                                      Prob > F      =   0.9707
Total (centered) SS     =  4058.298168                Centered R2   = -53.2172
Total (uncentered) SS   =  4058.673057                Uncentered R2 = -53.2122
Residual SS             =  220029.6103                Root MSE      =    7.319

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   repression |
          D1. |   -49.9278   9.313281    -5.36   0.000    -68.18149    -31.6741
              |
  _Iyear_1951 |  -.1963988   1.101825    -0.18   0.859    -2.355936    1.963138
  _Iyear_1952 |  -.5763908   1.089935    -0.53   0.597    -2.712624    1.559843
  _Iyear_1953 |  -.6118168   1.075894    -0.57   0.570    -2.720529    1.496896
  _Iyear_1954 |   .0778251    1.10577     0.07   0.944    -2.089443    2.245094
  _Iyear_1955 |   .4451696   1.090014     0.41   0.683    -1.691218    2.581557
  _Iyear_1956 |    .661569   1.168818     0.57   0.571    -1.629271     2.95241
  _Iyear_1957 |   1.271244   1.174844     1.08   0.279    -1.031408    3.573896
  _Iyear_1958 |   2.150721   1.333177     1.61   0.107    -.4622584      4.7637
  _Iyear_1959 |   1.618855    1.16112     1.39   0.163    -.6568983    3.894608
  _Iyear_1960 |   1.046883   1.117486     0.94   0.349    -1.143349    3.237115
  _Iyear_1961 |   1.301142   1.153319     1.13   0.259    -.9593218    3.561607
  _Iyear_1962 |   1.581518   1.161581     1.36   0.173    -.6951392    3.858175
  _Iyear_1963 |   1.119382   1.065911     1.05   0.294    -.9697651    3.208529
  _Iyear_1964 |   1.077633   1.129995     0.95   0.340    -1.137117    3.292383
  _Iyear_1965 |   1.716421   1.309045     1.31   0.190    -.8492607    4.282102
  _Iyear_1966 |   .5031064   1.184852     0.42   0.671     -1.81916    2.825373
  _Iyear_1967 |   .2721574   1.054219     0.26   0.796    -1.794073    2.338388
  _Iyear_1968 |   .1961156   1.011183     0.19   0.846    -1.785767    2.177998
  _Iyear_1969 |   .5364244   1.029713     0.52   0.602    -1.481776    2.554625
  _Iyear_1970 |     .79185    1.03102     0.77   0.442    -1.228912    2.812612
  _Iyear_1971 |   .7253161   1.094381     0.66   0.507     -1.41963    2.870263
  _Iyear_1972 |   .3503347   1.008336     0.35   0.728    -1.625968    2.326637
  _Iyear_1973 |   .2614622   1.145713     0.23   0.819    -1.984093    2.507018
  _Iyear_1974 |   .8134856   1.152957     0.71   0.480    -1.446268     3.07324
  _Iyear_1975 |   1.736395   1.149532     1.51   0.131    -.5166473    3.989437
  _Iyear_1976 |   1.549658   1.210972     1.28   0.201    -.8238034     3.92312
  _Iyear_1977 |   1.942731    1.21409     1.60   0.110    -.4368414    4.322304
  _Iyear_1978 |   1.329016   1.240158     1.07   0.284    -1.101649    3.759682
  _Iyear_1979 |   .3480495   1.175448     0.30   0.767    -1.955787    2.651886
  _Iyear_1980 |   .3127505   1.249288     0.25   0.802     -2.13581    2.761311
  _Iyear_1981 |   .8300198   1.244975     0.67   0.505    -1.610087    3.270126
  _Iyear_1982 |  -.0675423   1.221299    -0.06   0.956    -2.461245     2.32616
  _Iyear_1983 |  -.4393047   1.313887    -0.33   0.738    -3.014476    2.135867
  _Iyear_1984 |  -.9422103   1.211063    -0.78   0.437    -3.315851     1.43143
  _Iyear_1985 |  -.4647383   1.152624    -0.40   0.687    -2.723839    1.794363
  _Iyear_1986 |  -1.271593   1.235532    -1.03   0.303    -3.693192    1.150006
  _Iyear_1987 |  -1.625278   1.249567    -1.30   0.193    -4.074384    .8238276
  _Iyear_1988 |  -1.242397   1.421354    -0.87   0.382      -4.0282    1.543405
  _Iyear_1989 |  -.7944613   1.491569    -0.53   0.594    -3.717883     2.12896
  _Iyear_1990 |   .1280798   1.759641     0.07   0.942    -3.320753    3.576913
  _Iyear_1991 |   -1.18362   1.630495    -0.73   0.468    -4.379331    2.012091
  _Iyear_1992 |  -1.999923    1.59582    -1.25   0.210    -5.127672    1.127826
  _Iyear_1993 |   -1.80327   1.498344    -1.20   0.229     -4.73997     1.13343
  _Iyear_1994 |  -1.613435   1.485062    -1.09   0.277    -4.524104    1.297233
  _Iyear_1995 |  -1.440763   1.446594    -1.00   0.319    -4.276034    1.394508
  _Iyear_1996 |  -.5212807   1.246235    -0.42   0.676    -2.963857    1.921296
  _Iyear_1997 |    1.04974   1.318084     0.80   0.426    -1.533656    3.633136
  _Iyear_1998 |  -.5387504   1.417206    -0.38   0.704    -3.316423    2.238922
  _Iyear_1999 |   -2.30066   1.358941    -1.69   0.090    -4.964135    .3628158
  _Iyear_2000 |  -2.364256   1.484574    -1.59   0.111    -5.273969    .5454559
  _Iyear_2001 |   -1.61169   1.242877    -1.30   0.195    -4.047685    .8243046
  _Iyear_2002 |  -1.065688   1.386619    -0.77   0.442    -3.783411    1.652036
  _Iyear_2003 |  -.9686755   1.380484    -0.70   0.483    -3.674375    1.737024
  _Iyear_2004 |   .3050523   1.350888     0.23   0.821     -2.34264    2.952745
  _Iyear_2005 |   .3064416   1.312712     0.23   0.815    -2.266426    2.879309
  _Iyear_2006 |  -.2798163   1.469112    -0.19   0.849    -3.159223    2.599591
  _Iyear_2007 |  -.2745552   1.425889    -0.19   0.847    -3.069245    2.520135
  _Iyear_2008 |  -1.520056   1.464201    -1.04   0.299    -4.389836    1.349725
  _Iyear_2009 |   -1.56056   1.124109    -1.39   0.165    -3.763773    .6426523
              |
           lt |
          D1. |   .5729063   .2410316     2.38   0.017     .1004931     1.04532
          --. |   -.077628   .1904865    -0.41   0.684    -.4509748    .2957188
              |
       loggdp |
          D1. |  -2.816966   1.872605    -1.50   0.133    -6.487204     .853273
          --. |  -.4655204    .165452    -2.81   0.005    -.7898004   -.1412404
              |
        lpopl |
          D1. |   6.012382   8.613937     0.70   0.485    -10.87062    22.89539
          --. |   .3636876   .1655372     2.20   0.028     .0392407    .6881345
              |
seniorofficer |
          D1. |   2.660158    1.39936     1.90   0.057    -.0825378    5.402853
          --. |   .9873233   .4903055     2.01   0.044     .0263422    1.948304
              |
juniorofficer |
          D1. |   2.409797   2.277773     1.06   0.290    -2.054557    6.874151
          --. |   .5056793    .524641     0.96   0.335    -.5225982    1.533957
              |
        xpers |
          D1. |  -2.267033   1.482727    -1.53   0.126    -5.173125    .6390594
          --. |   1.131878   .6716442     1.69   0.092    -.1845199    2.448277
        _cons |  -.8363784   .9514183    -0.88   0.379    -2.701124    1.028367
-------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             28.455
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         29.371
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         D.repression
Included instruments: _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958
                      _Iyear_1959 _Iyear_1960 _Iyear_1961 _Iyear_1962
                      _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1966
                      _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970
                      _Iyear_1971 _Iyear_1972 _Iyear_1973 _Iyear_1974
                      _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978
                      _Iyear_1979 _Iyear_1980 _Iyear_1981 _Iyear_1982
                      _Iyear_1983 _Iyear_1984 _Iyear_1985 _Iyear_1986
                      _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990
                      _Iyear_1991 _Iyear_1992 _Iyear_1993 _Iyear_1994
                      _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998
                      _Iyear_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002
                      _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                      _Iyear_2007 _Iyear_2008 _Iyear_2009 D.lt lt D.loggdp
                      loggdp D.lpopl lpopl D.seniorofficer seniorofficer
                      D.juniorofficer juniorofficer D.xpers xpers
Excluded instruments: L.repression
Dropped collinear:    _Iyear_2010
------------------------------------------------------------------------------

.                 est store ecm1d

.                 xi:ivreg2 repress (d.repress=l.repress) i.year d.lt lt d.loggdp loggdp d.lpopl
>  lpopl d.civwar civwar ///
>                         d.intwar intwar d.mean5 mean5 d.senior senior d.junior junior d.xpers 
> xpers,rob bw(3)
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)
Warning - collinearities detected
Vars dropped:       _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954 _Iyear_1955
                    _Iyear_2010

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and autocorrelation
  kernel=Bartlett; bandwidth=     3
  time variable (t):  year
  group variable (i): gwf_caseid

                                                      Number of obs =     3884
                                                      F( 73,  3810) =     0.94
                                                      Prob > F      =   0.6264
Total (centered) SS     =  3867.293325                Centered R2   = -38.3607
Total (uncentered) SS   =  3869.076277                Uncentered R2 = -38.3425
Residual SS             =  152219.3097                Root MSE      =     6.26

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   repression |
          D1. |  -42.25285    7.38788    -5.72   0.000    -56.73283   -27.77288
              |
  _Iyear_1956 |   .0496278   .9763221     0.05   0.959    -1.863928    1.963184
  _Iyear_1957 |   .8899153   .9851166     0.90   0.366    -1.040878    2.820708
  _Iyear_1958 |   1.649919   1.122093     1.47   0.141    -.5493429    3.849181
  _Iyear_1959 |   1.163465   .9548772     1.22   0.223    -.7080599     3.03499
  _Iyear_1960 |   .7223804   .9321026     0.78   0.438    -1.104507    2.549268
  _Iyear_1961 |   1.075141    .974957     1.10   0.270    -.8357394    2.986022
  _Iyear_1962 |   1.318671   .9794148     1.35   0.178    -.6009472    3.238288
  _Iyear_1963 |   .8693509   .8991866     0.97   0.334    -.8930224    2.631724
  _Iyear_1964 |   .9519396   .9588232     0.99   0.321    -.9273193    2.831198
  _Iyear_1965 |   1.573363   1.090775     1.44   0.149    -.5645162    3.711243
  _Iyear_1966 |   .4670206   .9825928     0.48   0.635    -1.458826    2.392867
  _Iyear_1967 |   .1590616   .8866271     0.18   0.858    -1.578696    1.896819
  _Iyear_1968 |   .2359678   .8562181     0.28   0.783    -1.442189    1.914124
  _Iyear_1969 |   .5691908   .8699774     0.65   0.513    -1.135933    2.274315
  _Iyear_1970 |   .8196163   .8646003     0.95   0.343    -.8749692    2.514202
  _Iyear_1971 |    .938385   .9142187     1.03   0.305    -.8534507    2.730221
  _Iyear_1972 |   .5125714   .8439605     0.61   0.544    -1.141561    2.166704
  _Iyear_1973 |   .4504928   .9639275     0.47   0.640     -1.43877    2.339756
  _Iyear_1974 |   .8238971   .9669355     0.85   0.394    -1.071262    2.719056
  _Iyear_1975 |   1.735113   .9686817     1.79   0.073    -.1634681    3.633694
  _Iyear_1976 |   1.509621   1.027009     1.47   0.142    -.5032803    3.522521
  _Iyear_1977 |     1.7774   1.024811     1.73   0.083    -.2311935    3.785993
  _Iyear_1978 |   1.176852   1.047015     1.12   0.261    -.8752592    3.228964
  _Iyear_1979 |   .2629566   .9983316     0.26   0.792    -1.693737    2.219651
  _Iyear_1980 |   .3904174   1.047745     0.37   0.709    -1.663126    2.443961
  _Iyear_1981 |   .6951804   1.036587     0.67   0.502    -1.336494    2.726854
  _Iyear_1982 |  -.0978357   1.028242    -0.10   0.924    -2.113153    1.917482
  _Iyear_1983 |  -.2319714   1.103124    -0.21   0.833    -2.394054    1.930111
  _Iyear_1984 |  -.8345633   1.029102    -0.81   0.417    -2.851566    1.182439
  _Iyear_1985 |  -.3707683   .9755232    -0.38   0.704    -2.282759    1.541222
  _Iyear_1986 |   -1.25188   1.060036    -1.18   0.238    -3.329513    .8257531
  _Iyear_1987 |  -1.696829    1.07627    -1.58   0.115    -3.806279    .4126206
  _Iyear_1988 |   -1.48369   1.211417    -1.22   0.221    -3.858024     .890644
  _Iyear_1989 |  -1.110524   1.276749    -0.87   0.384    -3.612907    1.391859
  _Iyear_1990 |  -.2725409   1.478263    -0.18   0.854    -3.169883    2.624801
  _Iyear_1991 |   -1.43372   1.364436    -1.05   0.293    -4.107967    1.240526
  _Iyear_1992 |  -1.697153   1.378939    -1.23   0.218    -4.399824    1.005517
  _Iyear_1993 |  -1.532966   1.285396    -1.19   0.233    -4.052295    .9863642
  _Iyear_1994 |  -1.299009   1.271183    -1.02   0.307    -3.790482    1.192464
  _Iyear_1995 |  -1.038439   1.211584    -0.86   0.391      -3.4131    1.336222
  _Iyear_1996 |  -.4254121   1.057975    -0.40   0.688    -2.499006    1.648181
  _Iyear_1997 |   .7915593   1.131531     0.70   0.484    -1.426201    3.009319
  _Iyear_1998 |   -.381367   1.216007    -0.31   0.754    -2.764697    2.001963
  _Iyear_1999 |  -1.942105   1.147987    -1.69   0.091    -4.192117    .3079078
  _Iyear_2000 |  -1.988709    1.23122    -1.62   0.106    -4.401855    .4244383
  _Iyear_2001 |  -1.327144   1.046122    -1.27   0.205    -3.377505    .7232173
  _Iyear_2002 |   -.720258   1.171078    -0.62   0.539    -3.015529    1.575013
  _Iyear_2003 |   -.821184   1.172013    -0.70   0.484    -3.118288     1.47592
  _Iyear_2004 |     .15267    1.12707     0.14   0.892    -2.056347    2.361687
  _Iyear_2005 |    .381488   1.119165     0.34   0.733    -1.812034     2.57501
  _Iyear_2006 |    .206472   1.260708     0.16   0.870     -2.26447    2.677414
  _Iyear_2007 |   -.256904   1.182424    -0.22   0.828    -2.574412    2.060604
  _Iyear_2008 |  -1.246165   1.248559    -1.00   0.318    -3.693296    1.200966
  _Iyear_2009 |  -1.285822   .9718398    -1.32   0.186    -3.190593    .6189493
              |
           lt |
          D1. |   .5595848    .216418     2.59   0.010     .1354134    .9837563
          --. |  -.0986342   .1692358    -0.58   0.560    -.4303303    .2330618
              |
       loggdp |
          D1. |   -1.12829   1.677087    -0.67   0.501     -4.41532     2.15874
          --. |  -.6548589   .1792659    -3.65   0.000    -1.006214   -.3035043
              |
        lpopl |
          D1. |   11.84454   10.53832     1.12   0.261    -8.810186    32.49926
          --. |  -.1501751   .2073635    -0.72   0.469    -.5566002    .2562499
              |
       civwar |
          D1. |   1.553565   .9522797     1.63   0.103    -.3128689    3.419999
          --. |  -.0052299   .8380071    -0.01   0.995    -1.647694    1.637234
              |
       intwar |
          D1. |   2.307496   1.138178     2.03   0.043     .0767086    4.538283
          --. |   1.222867   .8855076     1.38   0.167    -.5126963     2.95843
              |
        mean5 |
          D1. |   2.654955   .8335836     3.18   0.001     1.021161    4.288748
          --. |   .6232353   .1796383     3.47   0.001     .2711506    .9753199
              |
seniorofficer |
          D1. |    2.94501    1.23618     2.38   0.017     .5221421    5.367878
          --. |   .6140815   .4260274     1.44   0.149    -.2209169     1.44908
              |
juniorofficer |
          D1. |   2.157009   1.938956     1.11   0.266    -1.643276    5.957293
          --. |   .1683289   .4506174     0.37   0.709    -.7148649    1.051523
              |
        xpers |
          D1. |  -2.188274   1.338545    -1.63   0.102    -4.811775    .4352259
          --. |    1.32207   .5873195     2.25   0.024      .170945    2.473195
        _cons |  -.8765733   .8184504    -1.07   0.284    -2.480707    .7275599
-------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             32.356
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         33.623
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         D.repression
Included instruments: _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959
                      _Iyear_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963
                      _Iyear_1964 _Iyear_1965 _Iyear_1966 _Iyear_1967
                      _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971
                      _Iyear_1972 _Iyear_1973 _Iyear_1974 _Iyear_1975
                      _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1979
                      _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983
                      _Iyear_1984 _Iyear_1985 _Iyear_1986 _Iyear_1987
                      _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991
                      _Iyear_1992 _Iyear_1993 _Iyear_1994 _Iyear_1995
                      _Iyear_1996 _Iyear_1997 _Iyear_1998 _Iyear_1999
                      _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003
                      _Iyear_2004 _Iyear_2005 _Iyear_2006 _Iyear_2007
                      _Iyear_2008 _Iyear_2009 D.lt lt D.loggdp loggdp D.lpopl
                      lpopl D.civwar civwar D.intwar intwar D.mean5 mean5
                      D.seniorofficer seniorofficer D.juniorofficer
                      juniorofficer D.xpers xpers
Excluded instruments: L.repression
Dropped collinear:    _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_2010
------------------------------------------------------------------------------

.                 est store ecm1e

.                 xi:ivreg2 repress (d.repress=l.repress) i.year d.lt lt d.loggdp loggdp d.lpopl
>  lpopl d.civwar civwar ///
>                         d.intwar intwar d.mean5 mean5 d.senior senior d.junior junior d.inst i
> nst d.xpers xpers,rob bw(3)
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)
Warning - collinearities detected
Vars dropped:       _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954 _Iyear_1955
                    _Iyear_2010

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and autocorrelation
  kernel=Bartlett; bandwidth=     3
  time variable (t):  year
  group variable (i): gwf_caseid

                                                      Number of obs =     3884
                                                      F( 75,  3808) =     0.94
                                                      Prob > F      =   0.6185
Total (centered) SS     =  3867.293325                Centered R2   = -37.1049
Total (uncentered) SS   =  3869.076277                Uncentered R2 = -37.0874
Residual SS             =    147362.89                Root MSE      =     6.16

-------------------------------------------------------------------------------
              |               Robust
   repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   repression |
          D1. |   -41.6141   7.164644    -5.81   0.000    -55.65655   -27.57166
              |
  _Iyear_1956 |  -.0655435    .961602    -0.07   0.946    -1.950249    1.819162
  _Iyear_1957 |   .7413994   .9632017     0.77   0.441    -1.146441     2.62924
  _Iyear_1958 |   1.458313   1.093766     1.33   0.182    -.6854287    3.602055
  _Iyear_1959 |   .9266453   .9380735     0.99   0.323     -.911945    2.765236
  _Iyear_1960 |   .5563184   .9150386     0.61   0.543    -1.237124    2.349761
  _Iyear_1961 |   .8634997      .9557     0.90   0.366    -1.009638    2.736637
  _Iyear_1962 |   1.127663   .9564112     1.18   0.238    -.7468681    3.002195
  _Iyear_1963 |   .7107178   .8800046     0.81   0.419    -1.014059    2.435495
  _Iyear_1964 |   .8589831   .9457201     0.91   0.364    -.9945943     2.71256
  _Iyear_1965 |   1.433438   1.062265     1.35   0.177    -.6485628    3.515438
  _Iyear_1966 |   .3198916   .9675217     0.33   0.741    -1.576416    2.216199
  _Iyear_1967 |   .0492675   .8721782     0.06   0.955     -1.66017    1.758705
  _Iyear_1968 |   .1100382   .8436978     0.13   0.896    -1.543579    1.763655
  _Iyear_1969 |   .4102809   .8569459     0.48   0.632    -1.269302    2.089864
  _Iyear_1970 |   .6577576   .8519143     0.77   0.440    -1.011964    2.327479
  _Iyear_1971 |   .8112804   .8993538     0.90   0.367    -.9514207    2.573982
  _Iyear_1972 |   .3556087   .8300747     0.43   0.668    -1.271308    1.982525
  _Iyear_1973 |   .3236364   .9463026     0.34   0.732    -1.531083    2.178355
  _Iyear_1974 |   .7097411   .9449994     0.75   0.453    -1.142424    2.561906
  _Iyear_1975 |   1.581861   .9478518     1.67   0.095     -.275894    3.439617
  _Iyear_1976 |   1.340226   1.005454     1.33   0.183     -.630427     3.31088
  _Iyear_1977 |   1.600771   1.002153     1.60   0.110    -.3634135    3.564956
  _Iyear_1978 |   1.035421   1.029246     1.01   0.314    -.9818644    3.052707
  _Iyear_1979 |   .1480785   .9871463     0.15   0.881    -1.786693     2.08285
  _Iyear_1980 |   .3119959   1.027311     0.30   0.761    -1.701497    2.325489
  _Iyear_1981 |   .5826534   1.024958     0.57   0.570    -1.426228    2.591535
  _Iyear_1982 |  -.1703753   1.015099    -0.17   0.867    -2.159933    1.819182
  _Iyear_1983 |  -.3426141   1.091716    -0.31   0.754    -2.482338     1.79711
  _Iyear_1984 |  -.9571003   1.019945    -0.94   0.348    -2.956156    1.041956
  _Iyear_1985 |  -.4374238   .9638249    -0.45   0.650    -2.326486    1.451638
  _Iyear_1986 |  -1.339495   1.045359    -1.28   0.200     -3.38836    .7093699
  _Iyear_1987 |  -1.748655   1.061451    -1.65   0.099    -3.829061    .3317499
  _Iyear_1988 |   -1.59611   1.198262    -1.33   0.183    -3.944661    .7524402
  _Iyear_1989 |  -1.205246   1.254246    -0.96   0.337    -3.663523     1.25303
  _Iyear_1990 |  -.3346406   1.456672    -0.23   0.818    -3.189665    2.520384
  _Iyear_1991 |  -1.494994   1.348627    -1.11   0.268    -4.138254    1.148267
  _Iyear_1992 |  -1.804983   1.365146    -1.32   0.186     -4.48062     .870653
  _Iyear_1993 |   -1.51808   1.255195    -1.21   0.226    -3.978216    .9420569
  _Iyear_1994 |  -1.346487   1.247036    -1.08   0.280    -3.790633    1.097658
  _Iyear_1995 |  -1.044185   1.175546    -0.89   0.374    -3.348213    1.259842
  _Iyear_1996 |   -.463598   1.029742    -0.45   0.653    -2.481856     1.55466
  _Iyear_1997 |   .7370293    1.10781     0.67   0.506    -1.434238    2.908297
  _Iyear_1998 |  -.3490937   1.193979    -0.29   0.770    -2.689249    1.991062
  _Iyear_1999 |  -1.887335   1.123531    -1.68   0.093    -4.089415    .3147449
  _Iyear_2000 |  -1.988475   1.210817    -1.64   0.101    -4.361632    .3846816
  _Iyear_2001 |  -1.342961   1.033106    -1.30   0.194    -3.367812    .6818907
  _Iyear_2002 |  -.7660232   1.151753    -0.67   0.506    -3.023418    1.491372
  _Iyear_2003 |  -.8024554   1.146288    -0.70   0.484    -3.049138    1.444228
  _Iyear_2004 |   .1186298   1.099801     0.11   0.914     -2.03694    2.274199
  _Iyear_2005 |   .3169859   1.096039     0.29   0.772    -1.831212    2.465184
  _Iyear_2006 |   .1707652   1.245227     0.14   0.891    -2.269834    2.611365
  _Iyear_2007 |  -.2716251   1.160024    -0.23   0.815     -2.54523     2.00198
  _Iyear_2008 |  -1.256532   1.216711    -1.03   0.302    -3.641242    1.128178
  _Iyear_2009 |  -1.290546   .9517779    -1.36   0.175    -3.155996    .5749046
              |
           lt |
          D1. |   .5403156    .213032     2.54   0.011     .1227806    .9578507
          --. |  -.1081512   .1670652    -0.65   0.517     -.435593    .2192905
              |
       loggdp |
          D1. |  -.9736071   1.630248    -0.60   0.550    -4.168834     2.22162
          --. |  -.6807712     .17959    -3.79   0.000    -1.032761   -.3287813
              |
        lpopl |
          D1. |   11.41873   10.44617     1.09   0.274    -9.055388    31.89285
          --. |   -.148718   .2043833    -0.73   0.467     -.549302    .2518659
              |
       civwar |
          D1. |   1.576895   .9312603     1.69   0.090    -.2483417    3.402132
          --. |   .0051625   .8178943     0.01   0.995    -1.597881    1.608206
              |
       intwar |
          D1. |    2.38682   1.136304     2.10   0.036     .1597058    4.613935
          --. |   1.128755   .8848644     1.28   0.202    -.6055471    2.863058
              |
        mean5 |
          D1. |   2.622111   .8187371     3.20   0.001     1.017416    4.226806
          --. |   .6432544     .17973     3.58   0.000     .2909901    .9955187
              |
seniorofficer |
          D1. |   2.856752   1.193497     2.39   0.017     .5175406    5.195963
          --. |   .5568515   .4172595     1.33   0.182     -.260962    1.374665
              |
juniorofficer |
          D1. |   2.052708   1.872167     1.10   0.273    -1.616671    5.722088
          --. |   .0652365   .4522948     0.14   0.885     -.821245     .951718
              |
 institutions |
          D1. |  -1.479529    .735961    -2.01   0.044    -2.921986   -.0370718
          --. |  -.5346228   .3826875    -1.40   0.162    -1.284676    .2154309
              |
        xpers |
          D1. |  -1.873616   1.326111    -1.41   0.158    -4.472745    .7255136
          --. |   1.436941   .5840558     2.46   0.014     .2922123    2.581669
        _cons |  -.5367451    .835173    -0.64   0.520    -2.173654    1.100164
-------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             33.415
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         34.684
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         D.repression
Included instruments: _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959
                      _Iyear_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963
                      _Iyear_1964 _Iyear_1965 _Iyear_1966 _Iyear_1967
                      _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971
                      _Iyear_1972 _Iyear_1973 _Iyear_1974 _Iyear_1975
                      _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1979
                      _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983
                      _Iyear_1984 _Iyear_1985 _Iyear_1986 _Iyear_1987
                      _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991
                      _Iyear_1992 _Iyear_1993 _Iyear_1994 _Iyear_1995
                      _Iyear_1996 _Iyear_1997 _Iyear_1998 _Iyear_1999
                      _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003
                      _Iyear_2004 _Iyear_2005 _Iyear_2006 _Iyear_2007
                      _Iyear_2008 _Iyear_2009 D.lt lt D.loggdp loggdp D.lpopl
                      lpopl D.civwar civwar D.intwar intwar D.mean5 mean5
                      D.seniorofficer seniorofficer D.juniorofficer
                      juniorofficer D.institutions institutions D.xpers xpers
Excluded instruments: L.repression
Dropped collinear:    _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_2010
------------------------------------------------------------------------------

.                 est store ecm1f

.                 label var repress "Repression"

.                 label var xpers "{bf:Personalism}"

.                 label var senior "Senior officer"

.                 label var junior "Junior officer"

.                 label var civwar "Civil conflict"

.                 label var intwar "Int'l conflict"

.                 label var mean5 "Protest"

.                 label var ld "Regime duration"

.                 label var lt `""Leader time" "in power""'

.                 label var loggdp "GDP per capita"

.                 label var lpopl "Population"

.                 label var inst "Institutions"

.                 coefplot (ecm1a, msym(d)) (ecm1b, msym(t)) (ecm1c, msym(oh)) (ecm1d, msym(plus
> )) (ecm1e, msym(P)) (ecm1f, msym(T)), ///
>                 title("Error-correction model", size(medsmall)) drop(_cons  _I* D*) order(xper
> s) xline(0) ///
>                 grid(glcolor(gs15)) mfcolor(white) xlabel(-2 (1) 4)   levels(95 90)  ///
>                 legend(off) ysize(2.) xsize(1.5)        xtitle("Long-run multiplier estimate",
>  height(6)) ///
>                 note("90 (thin) and 95 (thick) percent confidence intervals", size(vsmall) pos
> (6))
(note:  named style P not found in class symbol, default attributes used)

.                 graph export "$dir\repression-ecm.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\repression-ecm.pdf w
> ritten in PDF format)

. 
. 
. ***************************             
. * K-fold cross validation *
. ***************************
. * Show that RE model with coldwar indicator and NOT twoway-FE is better model fit *
.                 use temp,clear

.                 set seed $seed

.                         
.                         capture program drop crossvalrmse

.                         program define crossvalrmse
  1.                                 mat A=r(est)
  2.                                 mat U = J(rowsof(A),1,1)
  3.                                 mat c = U'*A
  4.                                 mat cm = c/rowsof(A)
  5.                                 mat list cm
  6.                         end

.                                                 
.                         capture program drop crosscalc

.                         program define crosscalc
  1.                                 qui: crossfold xtreg repression  i.year  loggdp lpopl, fe k
> (10)
  2.                                 crossvalrmse
  3.                                 mat cm2W=cm
  4.                                 qui: crossfold xtreg repression  coldwar  loggdp lpopl, fe 
> k(10)
  5.                                 crossvalrmse
  6.                                 mat cmFE=cm
  7.                                 qui: crossfold xtreg repression  coldwar  loggdp lpopl, re 
> k(10)
  8.                                 crossvalrmse
  9.                                 mat cmRE=cm
 10.                         end

.         
.                         simulate rmse2W =cm2W[1,1] rmseFE =cmFE[1,1] rmseRE =cmRE[1,1], rep(10
> 00):crosscalc

      command:  crosscalc
       rmse2W:  cm2W[1,1]
       rmseFE:  cmFE[1,1]
       rmseRE:  cmRE[1,1]

Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000

.                         gen med=.
(1,000 missing values generated)

.                         gen lo=.
(1,000 missing values generated)

.                         gen hi=.
(1,000 missing values generated)

.                         gen n=_n

.                         centile rmse2W, centile(2.5 50 97.5)

                                                       -- Binom. Interp. --
    Variable |       Obs  Percentile    Centile        [95% Conf. Interval]
-------------+-------------------------------------------------------------
      rmse2W |     1,000        2.5    .9408491        .9407152    .9409707
             |                   50    .9424236        .9423608    .9424982
             |                 97.5    .9444749        .9442608    .9448918

.                         replace lo = r(c_1) if n==1
(1 real change made)

.                         replace med = r(c_2) if n==1
(1 real change made)

.                         replace hi = r(c_3) if n==1
(1 real change made)

.                         centile rmseFE, centile(2.5 50 97.5)

                                                       -- Binom. Interp. --
    Variable |       Obs  Percentile    Centile        [95% Conf. Interval]
-------------+-------------------------------------------------------------
      rmseFE |     1,000        2.5    .8818106        .8817335     .881884
             |                   50     .882607        .8825695    .8826457
             |                 97.5    .8834883        .8834432    .8835706

.                         replace lo = r(c_1) if n==2
(1 real change made)

.                         replace med = r(c_2) if n==2
(1 real change made)

.                         replace hi = r(c_3) if n==2
(1 real change made)

.                         centile rmseRE, centile(2.5 50 97.5)

                                                       -- Binom. Interp. --
    Variable |       Obs  Percentile    Centile        [95% Conf. Interval]
-------------+-------------------------------------------------------------
      rmseRE |     1,000        2.5    .8629605        .8629161    .8629909
             |                   50    .8635541        .8635347    .8635759
             |                 97.5    .8640653        .8640124    .8641108

.                         replace lo = r(c_1) if n==3
(1 real change made)

.                         replace med = r(c_2) if n==3
(1 real change made)

.                         replace hi = r(c_3) if n==3
(1 real change made)

.                         replace hi = hi+.00075
(3 real changes made)

.                         replace lo = lo-.00075
(3 real changes made)

.                         twoway (rspike lo hi n if n<=3,title(Baseline specification))  (scatte
> r med n if n<=3,msym(plus)xscale(range(0.8 3.2)) ///
>                         xlab(1 "2-way FE" 2 "FE + Cold war" 3 "RE + Cold war") legend(off) yti
> tle(RMSE) ///
>                         xtitle(Estimator,size(med) height(6)) ylab(.84(.04).96))
(note:  named style med not found in class gsize, default attributes used)

.                         graph export "$dir\repression-estimator-rmse.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\repression-estimator
> -rmse.pdf written in PDF format)

.         
.                 * RE and FE yield similar estimates for personalism; OLS may be biased upwards
> : use RE for cross-validation *
.                 use temp,clear

.                 set seed $seed

.                 qui:xtreg repression  coldwar xpers lt loggdp lpopl $conflictvar $leadervar in
> st,cluster(gwf_caseid) fe

.                 lincom xpers 

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .5287804   .1585865     3.33   0.001     .2164974    .8410635
------------------------------------------------------------------------------

.                 qui xtreg repression  coldwar xpers lt loggdp lpopl $conflictvar $leadervar in
> st,cluster(gwf_caseid) re

.                 lincom xpers

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .5405523   .1474561     3.67   0.000     .2515437    .8295609
------------------------------------------------------------------------------

.                 qui reg repression  coldwar xpers lt loggdp lpopl $conflictvar $leadervar inst
> ,cluster(gwf_caseid) 

.                 lincom xpers

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .7443606   .1750149     4.25   0.000     .3997273    1.088994
------------------------------------------------------------------------------

.  
. * set globals *
.         set matsize 1000

.         global y = "repression"   /* outcome variable */

.         global id = "gwf_caseid"  /*cross-section unit */

.         global x = "coldwar loggdp lpopl"  /* baseline covariates */

.         global m = 1000 /* number of loops for the 10-fold cross-validation */

.         global v = 28 /* number of variables to test in the cross-validation tests */

.         global k =10 /* # folds for k-fold */

.         global varlist ="ythblgap polity nav_protestNV lt inherit junior divided logoil priord
> em support mparty grow gwf_military gwf_person legcomp gwf_party coup012 gwf_monarc excluded s
> enior G_age anocl xpers  lji intwar mean5 civwar nav_protestV"

. 
. * program to calculate the mean RMSE from one k-fold cross-validation with test variable *
.         capture program drop crossval

.         program define crossval
  1.                 qui:crossfold xtreg $y $x $test, re k($k)
  2.                 mat A=r(est)
  3.                 mat U = J(rowsof(A),1,1)
  4.                 mat c = U'*A
  5.                 mat cm = c/rowsof(A)
  6.         end

. 
. * cross-validate v varibles m times/loops to generate uncertainty estimates *
.         use temp,clear

.         set seed $seed

.         matrix sims = J($m,$v,.) 

.         matrix colnames sims = $varlist

.         forval m =1(1)$m {
  2.                 local i =1
  3.                 local var ="$varlist"
  4.                 foreach v of local var {
  5.                         global test = "`v'"
  6.                         qui:crossfold xtreg $y $x if $test~=., re k($k)  /* get baseline RM
> SE without test variable in specification */
  7.                         mat A=r(est)
  8.                         mat U = J(rowsof(A),1,1)
  9.                         mat c = U'*A
 10.                         mat xm = c/rowsof(A)   /* baseline RSME foreach of m simulations */
 11.                         global rmse = xm[1,1]
 12.                         qui crossval 
 13.                         qui mat sims[`m',`i'] = -1*($rmse - cm[1,1])  
 14.                         local i = `i'+1
 15.                 }
 16.                 di `m'
 17.         }
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. 
. * convert the sims stored in a matrix to sims stores as variable values for plotting *
.         local var ="$varlist"

.         foreach v of local var {
  2.                 gen sims_`v'=.
  3.         } 
(4,432 missing values generated)
(4,432 missing values generated)
(4,432 missing values generated)
(4,432 missing values generated)
(4,432 missing values generated)
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(4,432 missing values generated)
(4,432 missing values generated)
(4,432 missing values generated)
(4,432 missing values generated)
(4,432 missing values generated)
(4,432 missing values generated)

.         gen  n=_n

.         local i =1

.         local var ="$varlist"

.         foreach v of local var {
  2.                 forval m =1(1)$m {
  3.                         local sim=sims[`m',`i']  
  4.                         qui replace sims_`v'=`sim' if n==`m'
  5.                 } 
  6.                 local i = `i'+1
  7.         }

.         
. * calculate sims medians and uncertainty from sims stored as variable values *
.         gen test =""
(4,432 missing values generated)

.         gen med=.
(4,432 missing values generated)

.         gen hi=.
(4,432 missing values generated)

.         gen lo=.
(4,432 missing values generated)

.         local var ="$varlist"

.         local i =1

.         foreach v of local var {
  2.                 qui replace test = "`v'" if n==`i'
  3.                 qui centile sims_`v', centile(2.5 50 97.5)
  4.                 qui replace lo = r(c_1) if n==`i'
  5.                 qui replace med = r(c_2) if n==`i'
  6.                 qui replace hi = r(c_3) if n==`i'
  7.                 local i = `i'+1
  8.         } 

. 
. * Look at point estimates for change in RMSE, by test variable *
.         list n test med if n<=$v, clean noobs

     n            test         med  
     1        ythblgap    .0191637  
     2          polity    .0159763  
     3   nav_protestNV     .006358  
     4              lt     .003274  
     5         inherit    .0031363  
     6          junior    .0011186  
     7         divided    .0008552  
     8          logoil    .0008079  
     9        priordem    .0003757  
    10         support    .0002693  
    11          mparty   -.0001995  
    12            grow   -.0018187  
    13    gwf_military   -.0018704  
    14      gwf_person    -.001959  
    15         legcomp   -.0021317  
    16       gwf_party   -.0021784  
    17         coup012   -.0028558  
    18      gwf_monarc   -.0028516  
    19        excluded   -.0038337  
    20          senior   -.0056854  
    21           G_age   -.0057877  
    22           anocl   -.0065853  
    23           xpers   -.0109464  
    24             lji   -.0128961  
    25          intwar   -.0139235  
    26           mean5    -.016942  
    27          civwar   -.0170722  
    28    nav_protestV   -.0644407  

. 
. * Plot change in RMSE *
. twoway (rspike lo hi n if n<=$v,hori col(red)lwidth(thin)) (scatter n med if n<=$v,col(blue) m
> symbol(plus) msize(tiny) ///
>                 xtitle(Change in RMSE,height(6)) ytitle("") legend(off) xline(0) title(10-fold
>  cross-validation for test variables) ///
>                 ylab(1 "Youth bulge"  2 "Polity score"  3 "Non-violent protest" 4 "Leader time
> "    ///
>                 5 "Inherited party" 6  "Junior officer" 7 "Divided seizure group" 8 "Oil rents
> " 9 "Prior democracy" 10 "Support party" ///
>                 11 "Election" 12 "Economic growth" 13 "Military regime" 14 "Personalist regime
> " 15 "Legislative competition"   ///
>                 16 "Party regime" 17 "Coup" 18 "Monarchy" 19 "Ethnic exclusion" 20 "Senior mil
> itary officer" ///
>                 21 "Leader age" 22 "Anocracy" 23 "{bf:Personalism}" 24 "Judicial independence"
>  25 "Int'l conflict" ///
>                 26 "Protest" 27 "Civil conflict"  28 "Violent protest campaign") xlab(-.06(.02
> ).02)) ///
>                 (scatter n med if n==23,col(black)msize(medium)msymbol(plus)) 

.         graph export "$dir\repression-cross-validation.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\repression-cross-val
> idation.pdf written in PDF format)

.         
.         * RE model with best fit *
.                 xtreg repression coldwar civwar intwar nav_protestV mean5 loggdp lpopl lji xpe
> rs,re cluster(gwf_caseid) theta

Random-effects GLS regression                   Number of obs     =      3,877
Group variable: gwf_caseid                      Number of groups  =        245

R-sq:                                           Obs per group:
     within  = 0.2363                                         min =          1
     between = 0.3848                                         avg =       15.8
     overall = 0.4231                                         max =         53

                                                Wald chi2(9)      =     328.41
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

------------------- theta --------------------
  min      5%       median        95%      max
0.4637   0.5902     0.8119     0.9067   0.9131

                           (Std. Err. adjusted for 245 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     coldwar |    .292318   .0838732     3.49   0.000     .1279295    .4567065
      civwar |   .2609802    .055904     4.67   0.000     .1514104      .37055
      intwar |   .3177027   .1025755     3.10   0.002     .1166585    .5187469
nav_protestV |   .5229446   .0544985     9.60   0.000     .4161296    .6297596
       mean5 |   .0987401   .0312162     3.16   0.002     .0375575    .1599226
      loggdp |  -.3205374   .0837408    -3.83   0.000    -.4846663   -.1564085
       lpopl |   .4134276   .0744162     5.56   0.000     .2675744    .5592807
         lji |  -.9493692   .3680372    -2.58   0.010    -1.670709   -.2280295
       xpers |   .4486931   .1301786     3.45   0.001     .1935477    .7038384
       _cons |  -.2134345   .1381646    -1.54   0.122    -.4842321    .0573632
-------------+----------------------------------------------------------------
     sigma_u |  .64023647
     sigma_e |  .40676888
         rho |  .71242376   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                 est store r2

.                 xtreg repression coldwar civwar intwar nav_protestV mean5 loggdp lpopl lji xpe
> rs anocl,re cluster(gwf_caseid) theta

Random-effects GLS regression                   Number of obs     =      3,853
Group variable: gwf_caseid                      Number of groups  =        245

R-sq:                                           Obs per group:
     within  = 0.2302                                         min =          1
     between = 0.3804                                         avg =       15.7
     overall = 0.4185                                         max =         53

                                                Wald chi2(10)     =     316.16
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

------------------- theta --------------------
  min      5%       median        95%      max
0.4649   0.5912     0.8124     0.9070   0.9133

                           (Std. Err. adjusted for 245 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     coldwar |   .2967207   .0836094     3.55   0.000     .1328493     .460592
      civwar |   .2660861   .0571443     4.66   0.000     .1540854    .3780869
      intwar |   .3016749   .1064792     2.83   0.005     .0929796    .5103702
nav_protestV |    .515188    .054804     9.40   0.000     .4077741    .6226018
       mean5 |   .0965886   .0312987     3.09   0.002     .0352444    .1579328
      loggdp |   -.315054   .0855494    -3.68   0.000    -.4827278   -.1473802
       lpopl |    .408449   .0744542     5.49   0.000     .2625214    .5543767
         lji |  -.9681192   .3776978    -2.56   0.010    -1.708393   -.2278451
       xpers |   .4535791   .1292068     3.51   0.000     .2003385    .7068197
       anocl |   .0468663   .0656593     0.71   0.475    -.0818236    .1755563
       _cons |  -.2314313   .1383669    -1.67   0.094    -.5026255    .0397629
-------------+----------------------------------------------------------------
     sigma_u |  .64196401
     sigma_e |   .4066162
         rho |  .71368008   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                 est store r3    

.                 label var repress "Repression"

.                 label var xpers "{bf:Personalism}"

.                 label var civwar "Civil conflict"

.                 label var intwar "Int'l conflict"

.                 label var anocl "Anocracy"

.                 label var nav_protestV "Violent protest"

.                 label var loggdp "GDP per capita"

.                 label var lpopl "Population"

.                 label var coldwar "Cold war"

.                 label var mean5 "Protest"

.                 label var lji `""Judicial" "independence""'

.                 coefplot (r2, msymbol(d)) (r3, msymbol(t)), title("Best fit models" "RE with C
> old War indicator", size(medium)) ///
>                                 drop(_cons  _I*) order(xpers) xline(0) grid(glcolor(gs15)) mfc
> olor(white) xlabel(-1.5 (.5) .5) ///
>                                 levels(95 90) xtitle("Coefficient estimate", height(6)) xsize(
> 1.5) ysize(2) ///
>                                 legend(lab(3 "Drop Anocracy") lab(6 "Include Anocracy") pos(6)
>  ring(1) col(2))  ///
>                                 note("90 (thin) and 95 (thick) percent confidence intervals", 
> size(vsmall) pos(6))      

.                 graph export "$dir\repression-model-best-fit.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\repression-model-bes
> t-fit.pdf written in PDF format)

.                 
.                 qui xtreg repression coldwar civwar intwar loggdp lpopl nav_protestNV lji mean
> 5 xpers

.                 lincom xpers

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .4701815   .0487212     9.65   0.000     .3746897    .5656733
------------------------------------------------------------------------------

.                 qui reg repression coldwar civwar intwar loggdp lpopl nav_protestNV lji mean5 
> xpers

.                 lincom xpers    

 ( 1)  xpers = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .3944026   .0488526     8.07   0.000     .2986233    .4901818
------------------------------------------------------------------------------

.                 krls repression coldwar civwar intwar loggdp lpopl nav_protestNV lji mean5 xpe
> rs, deriv(d)              
Iteration =  1, Looloss: 3625.213  
Iteration =  2, Looloss: 3518.082  
Iteration =  3, Looloss: 3375.671  
Iteration =  4, Looloss: 3199.899  
Iteration =  5, Looloss: 3001.24   
Iteration =  6, Looloss: 2795.994  
Iteration =  7, Looloss: 2599.537  
Iteration =  8, Looloss: 2422.014  
Iteration =  9, Looloss: 2268.794  
Iteration = 10, Looloss: 2141.486  
Iteration = 11, Looloss: 2038.101  
Iteration = 12, Looloss: 1954.433  
Iteration = 13, Looloss: 1886.335  
Iteration = 14, Looloss: 1830.98   
Iteration = 15, Looloss: 1786.79   
Iteration = 16, Looloss: 1752.721  
Iteration = 17, Looloss: 1727.59   
Iteration = 18, Looloss: 1709.864  
Iteration = 19, Looloss: 1697.835  
Iteration = 20, Looloss: 1689.915  

Pointwise Derivatives                                       Number of obs =     3877 
                                                            Lambda        =    2.186 
                                                            Tolerance     =    3.877 
                                                            Sigma         =        9 
                                                            Eff. df       =    108.3 
                                                            R2            =    .5836 
                                                            Looloss       =     1682

    repression |      Avg.       SE        t    P>|t|        P25       P50       P75       
---------------+--------------------------------------------------------------------
      *coldwar |  .172989   .034911    4.955    0.000   -.037374   .153511   .377445  
       *civwar |  1.11876   .081445   13.736    0.000    .803908   1.26966   1.60518  
       *intwar |  .997898   .080595   12.382    0.000    .691067   1.19304    1.4791  
        loggdp | -.150267   .012461  -12.059    0.000   -.297491  -.159932    .01072  
         lpopl |  .300757    .01362   22.082    0.000    .167287   .293232   .436216  
*nav_protestNV |  .078131   .098423    0.794    0.427   -.245438   .068798   .441702  
           lji | -1.43546     .0881  -16.294    0.000   -2.48686  -1.41538  -.264042  
         mean5 |  .083297   .010132    8.221    0.000    .026757   .099321   .152768  
         xpers |  .261696   .043121    6.069    0.000   -.372514   .216152   .935841  
---------------+--------------------------------------------------------------------


.                 twoway lpolyci d_xpers year,xlab(1960(10)2000)ylab(-.2(.2).6) xtit(Year,height
> (6)) saving(h1.gph,replace) ///
>                 ytitle(Marginal effect of personalization on repression) legend(off) yline(0) 
> bw(1)
(note: file h1.gph not found)
(file h1.gph saved)

.                 gen ltd = ln(gwf_leader_duration)

.                 twoway lpolyci d_xpers ltd,  ylab(0(.2).6) xtitle(Leader time in power,height(
> 6))saving(h2.gph,replace) ///
>                 ytitle(Marginal effect of personalization on repression) legend(off) yline(0) 
> bw(.5)
(note: file h2.gph not found)
(file h2.gph saved)

.                 gr combine h1.gph h2.gph ,ysize(1) xsize(2.25)

.                 graph export "$dir\repression-kernel-best-fit.pdf",as(pdf) replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\repression-kernel-be
> st-fit.pdf written in PDF format)

.                 erase h1.gph 

.                 erase h2.gph

.                 
.                 
. *********************************************************************
. **** Models where personalism measure doesn't contain purge item ****
. *********************************************************************
.                 use temp,clear

.                 keep if year>=1955
(218 observations deleted)

.                 sum latent7 xpers

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
latent7_pe~m |      4,214    .4313493     .288594          0          1
       xpers |      4,214    .4257784    .2767921          0          1

.                 spearman latent7 xpers

 Number of obs =    4214
Spearman's rho =       0.9740

Test of Ho: latent7_personalism and xpers are independent
    Prob > |t| =       0.0000

.                 xi:qui ivreg2 repression i.gwf_caseid i.year latent7 lt loggdp lpopl $conflict
> var $leadervar institutions, cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1955-2010    (naturally coded; _Iyear_1955 omitted)

.                 lincom latent7

 ( 1)  latent7_personalism = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .3575476   .1290415     2.77   0.006     .1046308    .6104644
------------------------------------------------------------------------------

.                 est store nopurge1

.                 xtreg repression coldwar civwar intwar nav_protestV mean5 loggdp lpopl lji lat
> ent7,re cluster(gwf_caseid) theta

Random-effects GLS regression                   Number of obs     =      3,877
Group variable: gwf_caseid                      Number of groups  =        245

R-sq:                                           Obs per group:
     within  = 0.2317                                         min =          1
     between = 0.3822                                         avg =       15.8
     overall = 0.4175                                         max =         53

                                                Wald chi2(9)      =     316.72
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

------------------- theta --------------------
  min      5%       median        95%      max
0.4627   0.5893     0.8114     0.9065   0.9128

                                  (Std. Err. adjusted for 245 clusters in gwf_caseid)
-------------------------------------------------------------------------------------
                    |               Robust
         repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
            coldwar |   .2902011   .0838919     3.46   0.001     .1257759    .4546263
             civwar |   .2583548   .0559531     4.62   0.000     .1486887    .3680209
             intwar |   .3230964   .1057215     3.06   0.002     .1158862    .5303067
       nav_protestV |   .5247349   .0544542     9.64   0.000     .4180066    .6314632
              mean5 |   .0984494   .0314549     3.13   0.002     .0367989    .1600999
             loggdp |  -.3253865   .0837895    -3.88   0.000    -.4896109    -.161162
              lpopl |    .421223   .0745683     5.65   0.000     .2750719    .5673742
                lji |  -.9767729   .3687422    -2.65   0.008    -1.699494   -.2540515
latent7_personalism |   .3571852   .1216863     2.94   0.003     .1186845    .5956859
              _cons |  -.1708623   .1352468    -1.26   0.206    -.4359411    .0942166
--------------------+----------------------------------------------------------------
            sigma_u |  .64037315
            sigma_e |  .40792667
                rho |  .71134539   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------

.                 lincom latent7

 ( 1)  latent7_personalism = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .3571852   .1216863     2.94   0.003     .1186845    .5956859
------------------------------------------------------------------------------

.                 est store nopurge2

.                 label var repress "Repression"

.                 label var latent7 "{bf:Personalism-7}"

.                 label var anocl "Anocracy"

.                 label var nav_protestV "Violent protest"

.                 label var coldwar "Cold war"

.                 label var mean5 "Protest"

.                 label var lji `""Judicial" "independence""'

.                 label var senior "Senior officer"

.                 label var junior "Junior officer"

.                 label var civwar "Civil conflict"

.                 label var intwar "Int'l conflict"

.                 label var mean5 "Protest"

.                 label var ld "Regime duration"

.                 label var lt "Leader time in power"

.                 label var loggdp "GDP per capita"

.                 label var lpopl "Population"

.                 label var institution "Institutions"

.                 coefplot (nopurge1, msymbol(d)) (nopurge2, msymbol(t)), title(Seven item IRT m
> easure of Personalism, size(medium)) ///
>                                 drop(_cons  _I*) order(latent7) xline(0) grid(glcolor(gs15)) m
> fcolor(white) xlabel(-1.5 (.5) .5) ///
>                                 levels(95 90) xtitle("Coefficient estimate", height(6)) xsize(
> 1.5) ysize(2) ///
>                                 legend(lab(3 "Two-way FE") lab(6 "Best fit RE model") pos(6) r
> ing(1) col(2))  ///
>                                 note("90 (thin) and 95 (thick) percent confidence intervals", 
> size(vsmall) pos(6))

.                 graph export "$dir\repression-latent7.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\repression-latent7.p
> df written in PDF format)

.                 
. ***************************************************
. **** Models wwith subcomponents of personalism ****
. ***************************************************
.                 use temp,clear

.                 alpha $pvars1 $pvars2, std item gen(xpers_alpha)

Test scale = mean(standardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     correlation     alpha
-------------+-----------------------------------------------------------------
partyexcom~s | 4432    +       0.6288        0.4795          0.2828      0.7340
partyrbrstmp | 4432    +       0.6198        0.4682          0.2849      0.7360
officepers   | 4432    +       0.6861        0.5525          0.2694      0.7207
createparty  | 4432    +       0.5233        0.3510          0.3074      0.7565
milnotrial   | 4432    +       0.6457        0.5007          0.2788      0.7302
milmerit_p~B | 4432    +       0.6033        0.4478          0.2887      0.7397
paramil_pers | 4432    +       0.5548        0.3886          0.3000      0.7500
sectyapp_p~s | 4432    +       0.6415        0.4954          0.2798      0.7311
-------------+-----------------------------------------------------------------
Test scale   |                                               0.2865      0.7626
-------------------------------------------------------------------------------

.                 alpha $pvars1, std item gen(pers1)

Test scale = mean(standardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     correlation     alpha
-------------+-----------------------------------------------------------------
partyexcom~s | 4432    +       0.7873        0.5782          0.2747      0.5319
partyrbrstmp | 4432    +       0.7973        0.5949          0.2653      0.5199
officepers   | 4432    +       0.6837        0.4153          0.3733      0.6412
createparty  | 4432    +       0.5850        0.2778          0.4671      0.7245
-------------+-----------------------------------------------------------------
Test scale   |                                               0.3451      0.6782
-------------------------------------------------------------------------------

.                 alpha $pvars2, std item gen(pers2)

Test scale = mean(standardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     correlation     alpha
-------------+-----------------------------------------------------------------
milnotrial   | 4432    +       0.7235        0.4740          0.3277      0.5939
milmerit_p~B | 4432    +       0.7111        0.4550          0.3395      0.6066
paramil_pers | 4432    +       0.6918        0.4260          0.3577      0.6255
sectyapp_p~s | 4432    +       0.7160        0.4625          0.3348      0.6016
-------------+-----------------------------------------------------------------
Test scale   |                                               0.3399      0.6732
-------------------------------------------------------------------------------

.                 qui sum xpers_alpha

.                 replace xpers_alpha = (xpers_alpha+abs(r(min))) / (r(max) - r(min))
(4,432 real changes made)

.                 qui sum pers1

.                 replace pers1 = (pers1+abs(r(min))) / (r(max) - r(min))
(4,432 real changes made)

.                 qui sum pers2

.                 replace pers2 = (pers2+abs(r(min))) / (r(max) - r(min))
(4,432 real changes made)

.                 spearman xpers xpers_alpha pers1 pers2
(obs=4432)

             |    xpers xpers_~a    pers1    pers2
-------------+------------------------------------
       xpers |   1.0000 
 xpers_alpha |   0.9861   1.0000 
       pers1 |   0.8773   0.8751   1.0000 
       pers2 |   0.7891   0.8224   0.4630   1.0000 

.                 xi:qui ivreg2 repression i.gwf_caseid i.year xpers_alpha lt loggdp lpopl $conf
> lictvar $leadervar institutions, cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

.                 est store pp1

.                 xi:qui ivreg2 repression i.gwf_caseid i.year pers1 lt loggdp lpopl $conflictva
> r $leadervar institutions, cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

.                 lincom pers1

 ( 1)  pers1 = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .1586144   .1044196     1.52   0.129    -.0460442     .363273
------------------------------------------------------------------------------

.                 est store pp2

.                 xi:qui ivreg2 repression i.gwf_caseid i.year pers2 lt loggdp lpopl $conflictva
> r $leadervar institutions, cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

.                 lincom pers2

 ( 1)  pers2 = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |    .554886    .102407     5.42   0.000      .354172       .7556
------------------------------------------------------------------------------

.                 est store pp3

.                 xi:qui ivreg2 repression i.gwf_caseid i.year pers1 pers2 lt loggdp lpopl $conf
> lictvar $leadervar institutions, cluster(gwf_leaderid) partial(i.gwf_caseid i.year)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

.                 lincom pers1

 ( 1)  pers1 = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0132721   .1047137     0.13   0.899     -.191963    .2185071
------------------------------------------------------------------------------

.                 lincom pers2

 ( 1)  pers2 = 0

------------------------------------------------------------------------------
  repression |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .5508031   .1021323     5.39   0.000     .3506275    .7509788
------------------------------------------------------------------------------

.                 est store pp4

.                 label var repress "Repression"

.                 label var xpers_alpha "{bf:Personalism}"

.                 label var pers1 "{bf:Party personalism}"

.                 label var pers2 "{bf:Security personalism}"

.                 label var nav_protestV "Violent protest"

.                 label var coldwar "Cold war"

.                 label var mean5 "Protest"

.                 label var senior "Senior officer"

.                 label var junior "Junior officer"

.                 label var civwar "Civil conflict"

.                 label var intwar "Int'l conflict"

.                 label var mean5 "Protest"

.                 label var ld "Regime duration"

.                 label var lt "Leader time in power"

.                 label var loggdp "GDP per capita"

.                 label var lpopl "Population"

.                 label var institution "Institutions"

.                 coefplot (pp1, msymbol(d)) (pp2, msymbol(t)) (pp3, msymbol(s)) (pp4, msymbol(c
> )), ///
>                                 title(Sub-components of personalism index, size(medium)) ///
>                                 drop(_cons  _I*) order(latent7) xline(0) grid(glcolor(gs15)) m
> fcolor(white) xlabel(-1 (.5) 1) ///
>                                 levels(95 90) xtitle("Coefficient estimate", height(6)) xsize(
> 1.5) ysize(2) ///
>                                 legend(lab(3 "Full index") lab(6 "Party personalism only") lab
> (9 "Security personalism only") ///
>                                 lab(12 "Both sub-components") pos(6) ring(1) col(2) size(vsmal
> l))  ///
>                                 note("90 (thin) and 95 (thick) percent confidence intervals", 
> size(vsmall) pos(6))
(note:  named style c not found in class symbol, default attributes used)

.                 graph export "$dir\repression-pers-subcomponents.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\repression-pers-subc
> omponents.pdf written in PDF format)

.                         
.                                 
.  *********************************************************
.  **** Oster (2017) unobservable selection bounds test ****
.  *********************************************************
.         use temp,clear

.         xtset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1950 to 2010
                delta:  1 unit

.          * Bias reduction model: 2-way FE *
.         qui xtreg  repression xpers i.year loggdp lpopl lt $conflictvar $leadervar,fe

.          local rmax = e(r2)*1.3

.          psacalc delta xpers,rmax(`rmax')  

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       5.57063
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.47468                   0.021
Controlled   |        0.42652                   0.233
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.303
Beta         |    0.000000
Unr. Controls|   
-------------+----------------------------------------------------------------

.          psacalc beta xpers,rmax(`rmax') delta(1)

                 ---- Treatment Effect Estimate ----
             |     Estimate           Sq. difference      Bias changes
             |                      from controlled beta    direction
-------------+----------------------------------------------------------------
Beta         |       0.40165              .000618             
Alt. sol. 1  |     200.91650                40196             Yes
Alt. sol. 2  |                                                
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.            R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.47468         0.021
Controlled   |        0.42652         0.233
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.303
Delta        |   1.000
Unr. Controls|   
-------------+----------------------------------------------------------------

.          * Best fit specification: FE *
.         qui xtreg repression xpers coldwar loggdp lpopl $conflictvar lji nav_protestV,fe

.          local rmax = e(r2)*1.3

.          psacalc delta xpers,rmax(`rmax')  

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      15.82798
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.48936                   0.022
Controlled   |        0.44411                   0.239
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.311
Beta         |    0.000000
Unr. Controls|   
-------------+----------------------------------------------------------------

.          psacalc beta xpers,rmax(`rmax') delta(1)

                 ---- Treatment Effect Estimate ----
             |     Estimate           Sq. difference      Bias changes
             |                      from controlled beta    direction
-------------+----------------------------------------------------------------
Beta         |       0.42764              .000271             
Alt. sol. 1  |     242.39780                58542             Yes
Alt. sol. 2  |                                                
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.            R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.48936         0.022
Controlled   |        0.44411         0.239
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.311
Delta        |   1.000
Unr. Controls|   
-------------+----------------------------------------------------------------

.          * Best fit specification: OLS *
.         qui reg repression xpers coldwar loggdp lpopl $conflictvar lji nav_protestV,

.          local rmax = e(r2)*1.3

.          psacalc delta xpers,rmax(`rmax')  

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      63.12797
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.29338                   0.007
Controlled   |        0.33945                   0.481
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.625
Beta         |    0.000000
Unr. Controls|   
-------------+----------------------------------------------------------------

.          psacalc beta xpers,rmax(`rmax') delta(1)

                 ---- Treatment Effect Estimate ----
             |     Estimate           Sq. difference      Bias changes
             |                      from controlled beta    direction
-------------+----------------------------------------------------------------
Beta         |       0.35764              .000331             
Alt. sol. 1  |    -126.37376                16056             Yes
Alt. sol. 2  |                                                
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.            R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.29338         0.007
Controlled   |        0.33945         0.481
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.625
Delta        |   1.000
Unr. Controls|   
-------------+----------------------------------------------------------------

.          
.         gen n =_n

.         gen beta=.
(4,432 missing values generated)

.         gen rmax =.
(4,432 missing values generated)

.         gen pie =.
(4,432 missing values generated)

.         forval i = 1(1)50 {
  2.                 qui xtreg repression xpers i.year loggdp lpopl lt $conflictvar $leadervar,f
> e
  3.                 local rmax=e(r2)*(1+(`i'/25))
  4.                 qui replace rmax=`rmax' if n==`i'
  5.                 qui replace pie = 1+(`i'/25) if n==`i'
  6.                 qui psacalc beta xpers,  delta(1) rmax(`rmax')
  7.                 qui replace beta = r(beta) if n==`i'
  8.         }

.         twoway line beta pie if n<44,xtitle({&pi},size(large) height(6)) ///
>         ytit("{&beta}{sub:Personalism}",size(large) height(6)) ylin(0,lcol(red)) xlab(1(.5)2.5
> ) ///
>         xline(1.3,lcol(blue))  title("Given {&delta}=1")  ///
>         text(.27 1.175 "Oster (2017)" ,size(small)) ///
>         text(.25 1.175 "rule of thumb",size(small)) ///
>         text(.23 1.175 "{&pi}=1.3",size(small))  

.         graph export "$dir\sensitivity-analysis.pdf", as(pdf)   replace
(file C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\sensitivity-analysis
> .pdf written in PDF format)

. 
.  
.                 
. ******************************************************************
. ****** Address uncertainty in DV repression latent estimate ******
. ******************************************************************
. 
.  ************************************************************
.  ** First do this for the structural model with two-way FE **
.  ************************************************************
.         use temp,clear

.         set seed $seed

.         xtset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1950 to 2010
                delta:  1 unit

. 
. * Generate DV and IV with uncertainty in original dataset
.         gen latentmeansim = rnormal(latentmean,latentsd)

.         egen repressionsim = std(latentmeansim)

.         replace repressionsim = repressionsim*(-1)
(4,432 real changes made)

.         gen latentperssim = rnormal(pers_2pl,pers_se_2pl)

.         qui sum latentperssim

.         gen xperssim = (latentperssim+abs(r(min))) / (r(max) - r(min))

. 
. * Define a program for repreatedly generating DV and IV with uncertainty, and run regression m
> odel *
.         capture program drop latentsim

.         program define latentsim
  1.                 drop latentmeansim repressionsim latentperssim xperssim
  2.                 gen latentmeansim = rnormal(latentmean,latentsd)
  3.                 egen repressionsim = std(latentmeansim)
  4.                 replace repressionsim = repressionsim*(-1)
  5.                 gen latentperssim = rnormal(pers_2pl,pers_se_2pl)
  6.                 qui sum latentperssim
  7.                 gen xperssim = (latentperssim+abs(r(min))) / (r(max) - r(min))
  8.                 xi: ivreg2 repressionsim i.year i.gwf_caseid xperssim lt loggdp lpopl $conf
> lictvar $leadervar institution, cluster(gwf_leaderid)  partial(i.gwf_caseid i.year)
  9.         end

. 
. * Simulate 1000 times and store coefficients *
.         simulate _b _se, rep($m):latentsim

      command:  latentsim

Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000

. 
. * Save result to a dta file *
.         save simbeta.dta,replace
file simbeta.dta saved

. 
. * Calculate sims means and (between & within) standard errors from sims stored as variable val
> ues *
.         gen varname =""
(1,000 missing values generated)

.         gen meanbeta=.
(1,000 missing values generated)

.         gen varbeta=.
(1,000 missing values generated)

.         local var ="_b_seniorofficer _b_juniorofficer _b_institutions _b_lpopl _b_loggdp _b_me
> an5 _b_intwar _b_civwar _b_xperssim"

.         local i =1

.         gen n =_n

.         foreach v of local var {
  2.                 replace varname = "`v'" if n==`i'
  3.                 qui sum `v'
  4.                 replace meanbeta = r(mean) if n==`i'
  5.                 replace varbeta = r(Var) if n==`i'
  6.                 local i = `i'+1
  7.         }
variable varname was str1 now str16
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.         gen meanse=.
(1,000 missing values generated)

.         local var ="_se_seniorofficer _se_juniorofficer _se_institutions _se_lpopl _se_loggdp 
> _se_mean5 _se_intwar _se_civwar _se_xperssim"

.         local i =1

.         foreach v of local var {
  2.                 qui sum `v'
  3.                 replace meanse = r(mean) if n==`i'
  4.                 local i = `i'+1
  5.         }
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.         
. * Calculate overall standard errors *
.         qui count

.         gen simse = sqrt(meanse^2+varbeta*(1+1/r(N)))
(991 missing values generated)

. 
. 
.         /* 
>         Rubin (1987)
>         Estimate of beta is the mean of the betas from each estimated beta 
>         Estimate of variance is: Vb + Vw + Vb/m
>                 where Vb is the between variance, Vw is the mean variance, and Vb/m is the sam
> pling variance:
>                         Vb is the sum of the squared deviations from the mean of the estimated
>  betas
>                         Vw is the mean of the sampling variances (SE) from each of the m simlu
> ated variances
>         */ 
. 
. * Generate 95% CI *
.         gen lo = meanbeta - 1.96*simse
(991 missing values generated)

.         gen hi = meanbeta + 1.96*simse
(991 missing values generated)

. 
. * Plot coefficients with uncertainty *
.         twoway (rspike lo hi n if n<=9,hori col(black)lwidth(medium)) (scatter n meanbeta if n
> <=9,col(black) msymbol(d) msize(medium) ///
>                 xtitle("Coefficient estimate", height(6)) ytitle("") legend(off) ysize(1) xsiz
> e(1.5) xline(0,lpat(dash)) mfcolor(white) ///
>                 title("Correlates of domestic repression", size(medium) ) ///
>                 ylab(1 `" "Senior" "officer""' 2 `" "Junior" "officer""' 3 "Institutions" 4 "P
> opulation"  5 "GDP per capita" 6 "Protest"  ///
>                 7 "Int'l conflict" 8 "Civil conflict" 9 "{bf:Personalism}") ylabel(, angle(0))
> )  ///
>                 (scatter n meanbeta if n<=9,col(red)msize(vsmall)msymbol(plus))

.                 graph export "repression-model-3.pdf", as(pdf)   replace
(file repression-model-3.pdf written in PDF format)

.                 
.                 
.                 
.  ***************************************************
.  ** Second do this for the best-fit model with RE **
.  ****************************************************
.         use temp,clear

.         set seed $seed

.         xtset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1950 to 2010
                delta:  1 unit

. 
. * Generate DV and IV with uncertainty in original dataset
.         gen latentmeansim = rnormal(latentmean,latentsd)

.         egen repressionsim = std(latentmeansim)

.         replace repressionsim = repressionsim*(-1)
(4,432 real changes made)

.         gen latentperssim = rnormal(pers_2pl,pers_se_2pl)

.         qui sum latentperssim

.         gen xperssim = (latentperssim+abs(r(min))) / (r(max) - r(min))

. 
. * Define a program for repreatedly generating DV and IV with uncertainty, and run regression m
> odel *
.         capture program drop latentsim

.         program define latentsim
  1.                 drop latentmeansim repressionsim latentperssim xperssim
  2.                 gen latentmeansim = rnormal(latentmean,latentsd)
  3.                 egen repressionsim = std(latentmeansim)
  4.                 replace repressionsim = repressionsim*(-1)
  5.                 gen latentperssim = rnormal(pers_2pl,pers_se_2pl)
  6.                 qui sum latentperssim
  7.                 gen xperssim = (latentperssim+abs(r(min))) / (r(max) - r(min))
  8.                 xtreg repressionsim coldwar xperssim civwar intwar nav_protestV mean5 loggd
> p lpopl lji, cluster(gwf_caseid)   
  9.         end

. 
. * Simulate 1000 times and store coefficients *
.         simulate _b _se, rep($m):latentsim

      command:  latentsim

Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000

. 
. * Save result to a dta file *
.         save simbeta.dta,replace
file simbeta.dta saved

. 
. * Calculate sims means and (between & within) standard errors from sims stored as variable val
> ues *
.         gen varname =""
(1,000 missing values generated)

.         gen meanbeta=.
(1,000 missing values generated)

.         gen varbeta=.
(1,000 missing values generated)

.         local var ="_b_lji _b_mean5 _b_lpopl _b_loggdp _b_nav_protestV _b_intwar _b_civwar _b_
> coldwar _b_xperssim"

.         local i =1

.         gen n =_n

.         foreach v of local var {
  2.                 replace varname = "`v'" if n==`i'
  3.                 qui sum `v'
  4.                 replace meanbeta = r(mean) if n==`i'
  5.                 replace varbeta = r(Var) if n==`i'
  6.                 local i = `i'+1
  7.         }
variable varname was str1 now str6
(1 real change made)
(1 real change made)
(1 real change made)
variable varname was str6 now str8
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
variable varname was str8 now str9
(1 real change made)
(1 real change made)
(1 real change made)
variable varname was str9 now str15
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.         gen meanse=.
(1,000 missing values generated)

.         local var ="_se_lji _se_mean5 _se_lpopl _se_loggdp _se_nav_protestV _se_intwar _se_civ
> war _se_coldwar _se_xperssim"

.         local i =1

.         foreach v of local var {
  2.                 qui sum `v'
  3.                 replace meanse = r(mean) if n==`i'
  4.                 local i = `i'+1
  5.         }
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.         
. * Calculate overall standard errors *
.         qui count

.         gen simse = sqrt(meanse^2+varbeta*(1+1/r(N)))
(991 missing values generated)

. 
. * Generate 95% CI *
.         gen lo = meanbeta - 1.96*simse
(991 missing values generated)

.         gen hi = meanbeta + 1.96*simse
(991 missing values generated)

. 
. * Plot coefficients with uncertainty *
.         twoway (rspike lo hi n if n<=9,hori col(black)lwidth(medium)) (scatter n meanbeta if n
> <=9,col(black) msymbol(d) msize(medium) ///
>                 xtitle("Coefficient estimate", height(6)) ytitle("") legend(off) ysize(1) xsiz
> e(1.5) xline(0,lpat(dash)) mfcolor(white) ///
>                 title("Correlates of domestic repression", size(medium) ) ///
>                 ylab(1 `" "Judicial" "independence""' 2 "Protest " 3 "Population" 4 "GDP per c
> apita" 5 "Violent protest"  ///
>                 6 "Int'l conflict" 7 "Civil conflict" 8 "Cold War" 9 "{bf:Personalism}") ylabe
> l(, angle(0)))  ///
>                 (scatter n meanbeta if n<=9,col(red)msize(vsmall)msymbol(plus))

.                 graph export "repression-model-4.pdf", as(pdf)   replace
(file repression-model-4.pdf written in PDF format)

. 
. 
. 
.                 
.  ************** The End **************
.  
.  log close
      name:  <unnamed>
       log:  C:\Users\jgw12\Dropbox\Research\Structure\Personalism-repression\data\Strongman-Rep
> ression.log
  log type:  text
 closed on:  28 Nov 2018, 10:25:48
------------------------------------------------------------------------------------------------
